data_gen - AML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code by: \n",
    "# Karthikeya Kethamakka: kketham\n",
    "# Gautam Chauhan: gchauha\n",
    "# Naba Kishor Sahoo: nabsahoo\n",
    "# In order to run the code you might need to install dependencies along with datasets\n",
    "# To install dependencies follow the tutorial mentioned: https://www.youtube.com/watch?v=tzsft6lQUAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmentation Models: using `tf.keras` framework.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import nibabel as nib\n",
    "import glob\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tifffile import imsave\n",
    "from keras_applications.resnet import ResNet50\n",
    "import segmentation_models_3D as sm\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, BatchNormalization, Dropout, Lambda\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import models, layers, regularizers\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import regularizers\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import splitfolders \n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import random\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, BatchNormalization, Dropout, Lambda\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "import os\n",
    "import numpy as np\n",
    "from custom_datagen import imageLoader\n",
    "#import tensorflow as tf\n",
    "import keras as K\n",
    "from matplotlib import pyplot as plt\n",
    "import glob\n",
    "import random\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import random\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_DATASET_PATH = 'BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1854.603271484375\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "test_image_flair=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_flair.nii').get_fdata()\n",
    "print(test_image_flair.max())\n",
    "test_image_flair=scaler.fit_transform(test_image_flair.reshape(-1, test_image_flair.shape[-1])).reshape(test_image_flair.shape)\n",
    "print(test_image_flair.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image_t1=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_t1.nii').get_fdata()\n",
    "test_image_t1=scaler.fit_transform(test_image_t1.reshape(-1, test_image_t1.shape[-1])).reshape(test_image_t1.shape)\n",
    "\n",
    "test_image_t1ce=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_t1ce.nii').get_fdata()\n",
    "test_image_t1ce=scaler.fit_transform(test_image_t1ce.reshape(-1, test_image_t1ce.shape[-1])).reshape(test_image_t1ce.shape)\n",
    "\n",
    "test_image_t2=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_t2.nii').get_fdata()\n",
    "test_image_t2=scaler.fit_transform(test_image_t2.reshape(-1, test_image_t2.shape[-1])).reshape(test_image_t2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_mask=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_seg.nii').get_fdata()\n",
    "test_mask=test_mask.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.unique(test_mask))  \n",
    "test_mask[test_mask==4] = 3  \n",
    "print(np.unique(test_mask)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test_mask' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_15984/3735516714.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mn_slice\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m231\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_image_flair\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn_slice\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'gray'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Image flair'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'test_mask' is not defined"
     ]
    }
   ],
   "source": [
    "n_slice=random.randint(0, test_mask.shape[2])\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(231)\n",
    "plt.imshow(test_image_flair[:,:,n_slice], cmap='gray')\n",
    "plt.title('Image flair')\n",
    "plt.subplot(232)\n",
    "plt.imshow(test_image_t1[:,:,n_slice], cmap='gray')\n",
    "plt.title('Image t1')\n",
    "plt.subplot(233)\n",
    "plt.imshow(test_image_t1ce[:,:,n_slice], cmap='gray')\n",
    "plt.title('Image t1ce')\n",
    "plt.subplot(234)\n",
    "plt.imshow(test_image_t2[:,:,n_slice], cmap='gray')\n",
    "plt.title('Image t2')\n",
    "plt.subplot(235)\n",
    "plt.imshow(test_mask[:,:,n_slice])\n",
    "plt.title('Mask')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "combined_x = np.stack([test_image_flair, test_image_t1ce, test_image_t2], axis=3)\n",
    "print(combined_x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "combined_x=combined_x[56:184, 56:184, 13:141]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "test_mask = test_mask[56:184, 56:184, 13:141]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_slice=random.randint(0, test_mask.shape[2])\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.imshow(combined_x[:,:,n_slice, 0], cmap='gray')\n",
    "plt.title('Image flair')\n",
    "plt.subplot(222)\n",
    "plt.imshow(combined_x[:,:,n_slice, 1], cmap='gray')\n",
    "plt.title('Image t1ce')\n",
    "plt.subplot(223)\n",
    "plt.imshow(combined_x[:,:,n_slice, 2], cmap='gray')\n",
    "plt.title('Image t2')\n",
    "plt.subplot(224)\n",
    "plt.imshow(test_mask[:,:,n_slice])\n",
    "plt.title('Mask')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imsave('BraTS2020_TrainingData/combined255.tif', combined_x)\n",
    "np.save('BraTS2020_TrainingData/combined255.npy', combined_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_img=np.load('BraTS2020_TrainingData/combined255.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_mask = to_categorical(test_mask, num_classes=4)\n",
    "test_mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#t1_list = sorted(glob.glob('BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*t1.nii'))\n",
    "t2_list = sorted(glob.glob('BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*t2.nii'))\n",
    "t1ce_list = sorted(glob.glob('BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*t1ce.nii'))\n",
    "flair_list = sorted(glob.glob('BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*flair.nii'))\n",
    "mask_list = sorted(glob.glob('BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*seg.nii'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(t2_list)\n",
    "len(mask_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for img in range(len(t2_list)):   #Using t1_list as all lists are of same size\n",
    "    print(\"Now preparing image and masks number: \", img)\n",
    "      \n",
    "    temp_image_t2=nib.load(t2_list[img]).get_fdata()\n",
    "    temp_image_t2=scaler.fit_transform(temp_image_t2.reshape(-1, temp_image_t2.shape[-1])).reshape(temp_image_t2.shape)\n",
    "   \n",
    "    temp_image_t1ce=nib.load(t1ce_list[img]).get_fdata()\n",
    "    temp_image_t1ce=scaler.fit_transform(temp_image_t1ce.reshape(-1, temp_image_t1ce.shape[-1])).reshape(temp_image_t1ce.shape)\n",
    "   \n",
    "    temp_image_flair=nib.load(flair_list[img]).get_fdata()\n",
    "    temp_image_flair=scaler.fit_transform(temp_image_flair.reshape(-1, temp_image_flair.shape[-1])).reshape(temp_image_flair.shape)\n",
    "        \n",
    "    temp_mask=nib.load(mask_list[img]).get_fdata()\n",
    "    temp_mask=temp_mask.astype(np.uint8)\n",
    "    temp_mask[temp_mask==4] = 3  \n",
    "\n",
    "    \n",
    "    \n",
    "    temp_combined_images = np.stack([temp_image_flair, temp_image_t1ce, temp_image_t2], axis=3)\n",
    "    \n",
    "\n",
    "    temp_combined_images=temp_combined_images[56:184, 56:184, 13:141]\n",
    "    temp_mask = temp_mask[56:184, 56:184, 13:141]\n",
    "    \n",
    "    val, counts = np.unique(temp_mask, return_counts=True)\n",
    "    \n",
    "    if (1 - (counts[0]/counts.sum())) > 0.01:\n",
    "        print(\"Save\")\n",
    "        temp_mask= to_categorical(temp_mask, num_classes=4)\n",
    "        np.save('BraTS2020_TrainingData/input_data_3channels/images/image_'+str(img)+'.npy', temp_combined_images)\n",
    "        np.save('BraTS2020_TrainingData/input_data_3channels/masks/mask_'+str(img)+'.npy', temp_mask)\n",
    "        \n",
    "    else:\n",
    "        print(\"No Use\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'splitfolders' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_9148/86579887.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0moutput_folder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'BraTS2020_TrainingData/input_data_128/'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msplitfolders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mratio\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_folder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moutput_folder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mratio\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m.75\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m.25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroup_prefix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# default values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'splitfolders' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "input_folder = 'BraTS2020_TrainingData/input_data_3channels/'\n",
    "output_folder = 'BraTS2020_TrainingData/input_data_128/'\n",
    "\n",
    "splitfolders.ratio(input_folder, output=output_folder, seed=42, ratio=(.75, .25), group_prefix=None) # default values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "columns = ['0','1', '2', '3']\n",
    "df = pd.DataFrame(columns=columns)\n",
    "train_mask_list = sorted(glob.glob('BraTS2020_TrainingData/input_data_128/train/masks/*.npy'))\n",
    "for img in range(0,10):\n",
    "    print(img)\n",
    "    temp_image=np.load(train_mask_list[img])\n",
    "    temp_image = np.argmax(temp_image, axis=3)\n",
    "    val, counts = np.unique(temp_image, return_counts=True)\n",
    "    zipped = zip(columns, counts)\n",
    "    conts_dict = dict(zipped)\n",
    "    \n",
    "    df = df.append(conts_dict, ignore_index=True)\n",
    "\n",
    "label_0 = df['0'].sum()\n",
    "label_1 = df['1'].sum()\n",
    "label_2 = df['1'].sum()\n",
    "label_3 = df['3'].sum()\n",
    "total_labels = label_0 + label_1 + label_2 + label_3\n",
    "n_classes = 4\n",
    "\n",
    "wt0 = round((total_labels/(n_classes*label_0)), 2) \n",
    "wt1 = round((total_labels/(n_classes*label_1)), 2)\n",
    "wt2 = round((total_labels/(n_classes*label_2)), 2)\n",
    "wt3 = round((total_labels/(n_classes*label_3)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25 54.34 54.34 27.97\n"
     ]
    }
   ],
   "source": [
    "print(wt0,wt1,wt2,wt3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20143940"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94380"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94380"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_img(img_dir, img_list):\n",
    "    images=[]\n",
    "    for i, image_name in enumerate(img_list):    \n",
    "        if (image_name.split('.')[1] == 'npy'):\n",
    "            \n",
    "            image = np.load(img_dir+image_name)\n",
    "                      \n",
    "            images.append(image)\n",
    "    images = np.array(images)\n",
    "    \n",
    "    return(images)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imageLoader(img_dir, img_list, mask_dir, mask_list, batch_size):\n",
    "\n",
    "    L = len(img_list)\n",
    "\n",
    "    while True:\n",
    "\n",
    "        batch_start = 0\n",
    "        batch_end = batch_size\n",
    "\n",
    "        while batch_start < L:\n",
    "            limit = min(batch_end, L)\n",
    "                       \n",
    "            X = load_img(img_dir, img_list[batch_start:limit])\n",
    "            Y = load_img(mask_dir, mask_list[batch_start:limit])\n",
    "\n",
    "            yield (X,Y)  \n",
    "\n",
    "            batch_start += batch_size   \n",
    "            batch_end += batch_size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import random\n",
    "train_img_dir = \"BraTS2020_TrainingData/input_data_128/train/images/\"\n",
    "train_mask_dir = \"BraTS2020_TrainingData/input_data_128/train/masks/\"\n",
    "train_img_list=os.listdir(train_img_dir)\n",
    "train_mask_list = os.listdir(train_mask_dir)\n",
    "\n",
    "batch_size = 2\n",
    "\n",
    "train_img_datagen = imageLoader(train_img_dir, train_img_list, \n",
    "                                train_mask_dir, train_mask_list, batch_size)\n",
    "\n",
    "img, msk = train_img_datagen.__next__()\n",
    "\n",
    "\n",
    "img_num = random.randint(0,img.shape[0]-1)\n",
    "test_img=img[img_num]\n",
    "test_mask=msk[img_num]\n",
    "test_mask=np.argmax(test_mask, axis=3)\n",
    "\n",
    "n_slice=random.randint(0, test_mask.shape[2])\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.imshow(test_img[:,:,n_slice, 0], cmap='gray')\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.imshow(test_img[:,:,n_slice, 1], cmap='gray')\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.imshow(test_img[:,:,n_slice, 2], cmap='gray')\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.imshow(test_mask[:,:,n_slice])\n",
    "plt.title('Mask')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 128, 128, 1  0           []                               \n",
      "                                28, 3)]                                                           \n",
      "                                                                                                  \n",
      " conv3d (Conv3D)                (None, 128, 128, 12  1312        ['input_1[0][0]']                \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 128, 128, 12  0           ['conv3d[0][0]']                 \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " conv3d_1 (Conv3D)              (None, 128, 128, 12  6928        ['dropout[0][0]']                \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " max_pooling3d (MaxPooling3D)   (None, 64, 64, 64,   0           ['conv3d_1[0][0]']               \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_2 (Conv3D)              (None, 64, 64, 64,   13856       ['max_pooling3d[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 64, 64, 64,   0           ['conv3d_2[0][0]']               \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_3 (Conv3D)              (None, 64, 64, 64,   27680       ['dropout_1[0][0]']              \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " max_pooling3d_1 (MaxPooling3D)  (None, 32, 32, 32,   0          ['conv3d_3[0][0]']               \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_4 (Conv3D)              (None, 32, 32, 32,   55360       ['max_pooling3d_1[0][0]']        \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, 32, 32, 32,   0           ['conv3d_4[0][0]']               \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_5 (Conv3D)              (None, 32, 32, 32,   110656      ['dropout_2[0][0]']              \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " max_pooling3d_2 (MaxPooling3D)  (None, 16, 16, 16,   0          ['conv3d_5[0][0]']               \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_6 (Conv3D)              (None, 16, 16, 16,   221312      ['max_pooling3d_2[0][0]']        \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)            (None, 16, 16, 16,   0           ['conv3d_6[0][0]']               \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv3d_7 (Conv3D)              (None, 16, 16, 16,   442496      ['dropout_3[0][0]']              \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " max_pooling3d_3 (MaxPooling3D)  (None, 8, 8, 8, 128  0          ['conv3d_7[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv3d_8 (Conv3D)              (None, 8, 8, 8, 256  884992      ['max_pooling3d_3[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " dropout_4 (Dropout)            (None, 8, 8, 8, 256  0           ['conv3d_8[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv3d_9 (Conv3D)              (None, 8, 8, 8, 256  1769728     ['dropout_4[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv3d_transpose (Conv3DTransp  (None, 16, 16, 16,   262272     ['conv3d_9[0][0]']               \n",
      " ose)                           128)                                                              \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 16, 16, 16,   0           ['conv3d_transpose[0][0]',       \n",
      "                                256)                              'conv3d_7[0][0]']               \n",
      "                                                                                                  \n",
      " conv3d_10 (Conv3D)             (None, 16, 16, 16,   884864      ['concatenate[0][0]']            \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " dropout_5 (Dropout)            (None, 16, 16, 16,   0           ['conv3d_10[0][0]']              \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv3d_11 (Conv3D)             (None, 16, 16, 16,   442496      ['dropout_5[0][0]']              \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv3d_transpose_1 (Conv3DTran  (None, 32, 32, 32,   65600      ['conv3d_11[0][0]']              \n",
      " spose)                         64)                                                               \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 32, 32, 32,   0           ['conv3d_transpose_1[0][0]',     \n",
      "                                128)                              'conv3d_5[0][0]']               \n",
      "                                                                                                  \n",
      " conv3d_12 (Conv3D)             (None, 32, 32, 32,   221248      ['concatenate_1[0][0]']          \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " dropout_6 (Dropout)            (None, 32, 32, 32,   0           ['conv3d_12[0][0]']              \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_13 (Conv3D)             (None, 32, 32, 32,   110656      ['dropout_6[0][0]']              \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_transpose_2 (Conv3DTran  (None, 64, 64, 64,   16416      ['conv3d_13[0][0]']              \n",
      " spose)                         32)                                                               \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate)    (None, 64, 64, 64,   0           ['conv3d_transpose_2[0][0]',     \n",
      "                                64)                               'conv3d_3[0][0]']               \n",
      "                                                                                                  \n",
      " conv3d_14 (Conv3D)             (None, 64, 64, 64,   55328       ['concatenate_2[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " dropout_7 (Dropout)            (None, 64, 64, 64,   0           ['conv3d_14[0][0]']              \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_15 (Conv3D)             (None, 64, 64, 64,   27680       ['dropout_7[0][0]']              \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_transpose_3 (Conv3DTran  (None, 128, 128, 12  4112       ['conv3d_15[0][0]']              \n",
      " spose)                         8, 16)                                                            \n",
      "                                                                                                  \n",
      " concatenate_3 (Concatenate)    (None, 128, 128, 12  0           ['conv3d_transpose_3[0][0]',     \n",
      "                                8, 32)                            'conv3d_1[0][0]']               \n",
      "                                                                                                  \n",
      " conv3d_16 (Conv3D)             (None, 128, 128, 12  13840       ['concatenate_3[0][0]']          \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " dropout_8 (Dropout)            (None, 128, 128, 12  0           ['conv3d_16[0][0]']              \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " conv3d_17 (Conv3D)             (None, 128, 128, 12  6928        ['dropout_8[0][0]']              \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " conv3d_18 (Conv3D)             (None, 128, 128, 12  68          ['conv3d_17[0][0]']              \n",
      "                                8, 4)                                                             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 5,645,828\n",
      "Trainable params: 5,645,828\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "(None, 128, 128, 128, 3)\n",
      "(None, 128, 128, 128, 4)\n"
     ]
    }
   ],
   "source": [
    "def iou(y_true, y_pred):\n",
    "    def f(y_true, y_pred):\n",
    "        intersection = (y_true * y_pred).sum()\n",
    "        union = y_true.sum() + y_pred.sum() - intersection\n",
    "        x = (intersection + 1e-15) / (union + 1e-15)\n",
    "        x = x.astype(np.float32)\n",
    "        return x\n",
    "\n",
    "    return tf.numpy_function(f, [y_true, y_pred], tf.float32)\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, BatchNormalization, Dropout, Lambda\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import models, layers, regularizers\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "metrics = [\"acc\", tf.keras.metrics.Recall(), tf.keras.metrics.Precision(), iou]\n",
    "def weighted_categorical_crossentropy(weights):\n",
    "    def wcce(y_true, y_pred):\n",
    "        Kweights = K.constant(weights)\n",
    "        y_true = K.cast(y_true, y_pred.dtype)\n",
    "        return K.categorical_crossentropy(y_true, y_pred) * K.sum(y_true * Kweights, axis=-1)\n",
    "    return wcce\n",
    "loss=weighted_categorical_crossentropy([0.25,54.34,54.34,27.97])\n",
    "\n",
    "kernel_initializer =  'he_uniform' \n",
    "\n",
    "def simple_unet_model(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):\n",
    "    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS))\n",
    "    s = inputs\n",
    "    c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(s)\n",
    "    c1 = Dropout(0.1)(c1)\n",
    "    c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c1)\n",
    "    p1 = MaxPooling3D((2, 2, 2))(c1)\n",
    "    \n",
    "    c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p1)\n",
    "    c2 = Dropout(0.1)(c2)\n",
    "    c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c2)\n",
    "    p2 = MaxPooling3D((2, 2, 2))(c2)\n",
    "     \n",
    "    c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p2)\n",
    "    c3 = Dropout(0.2)(c3)\n",
    "    c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c3)\n",
    "    p3 = MaxPooling3D((2, 2, 2))(c3)\n",
    "     \n",
    "    c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p3)\n",
    "    c4 = Dropout(0.2)(c4)\n",
    "    c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c4)\n",
    "    p4 = MaxPooling3D(pool_size=(2, 2, 2))(c4)\n",
    "     \n",
    "    c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p4)\n",
    "    c5 = Dropout(0.3)(c5)\n",
    "    c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c5)\n",
    "    \n",
    "    #Expansive path \n",
    "    u6 = Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(c5)\n",
    "    u6 = concatenate([u6, c4])\n",
    "    c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u6)\n",
    "    c6 = Dropout(0.2)(c6)\n",
    "    c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c6)\n",
    "     \n",
    "    u7 = Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(c6)\n",
    "    u7 = concatenate([u7, c3])\n",
    "    c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u7)\n",
    "    c7 = Dropout(0.2)(c7)\n",
    "    c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c7)\n",
    "     \n",
    "    u8 = Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(c7)\n",
    "    u8 = concatenate([u8, c2])\n",
    "    c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u8)\n",
    "    c8 = Dropout(0.1)(c8)\n",
    "    c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c8)\n",
    "     \n",
    "    u9 = Conv3DTranspose(16, (2, 2, 2), strides=(2, 2, 2), padding='same')(c8)\n",
    "    u9 = concatenate([u9, c1])\n",
    "    c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u9)\n",
    "    c9 = Dropout(0.1)(c9)\n",
    "    c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c9)\n",
    "     \n",
    "    outputs = Conv3D(num_classes, (1, 1, 1), activation='softmax')(c9)\n",
    "     \n",
    "    model = Model(inputs=[inputs], outputs=[outputs])\n",
    "    model.summary()\n",
    "    \n",
    "    return model\n",
    "model = simple_unet_model(128, 128, 128, 3, 4)\n",
    "print(model.input_shape)\n",
    "print(model.output_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_img_dir = \"BraTS2020_TrainingData/input_data_128/train/images/\"\n",
    "train_mask_dir = \"BraTS2020_TrainingData/input_data_128/train/masks/\"\n",
    "\n",
    "img_list = os.listdir(train_img_dir)\n",
    "msk_list = os.listdir(train_mask_dir)\n",
    "\n",
    "num_images = len(os.listdir(train_img_dir))\n",
    "\n",
    "img_num = random.randint(0,num_images-1)\n",
    "test_img = np.load(train_img_dir+img_list[img_num])\n",
    "test_mask = np.load(train_mask_dir+msk_list[img_num])\n",
    "test_mask = np.argmax(test_mask, axis=3)\n",
    "\n",
    "n_slice=random.randint(0, test_mask.shape[2])\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.imshow(test_img[:,:,n_slice, 0], cmap='gray')\n",
    "plt.title('Image flair')\n",
    "plt.subplot(222)\n",
    "plt.imshow(test_img[:,:,n_slice, 1], cmap='gray')\n",
    "plt.title('Image t1ce')\n",
    "plt.subplot(223)\n",
    "plt.imshow(test_img[:,:,n_slice, 2], cmap='gray')\n",
    "plt.title('Image t2')\n",
    "plt.subplot(224)\n",
    "plt.imshow(test_mask[:,:,n_slice])\n",
    "plt.title('Mask')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_img_dir = \"BraTS2020_TrainingData/input_data_128/train/images/\"\n",
    "train_mask_dir = \"BraTS2020_TrainingData/input_data_128/train/masks/\"\n",
    "\n",
    "val_img_dir = \"BraTS2020_TrainingData/input_data_128/val/images/\"\n",
    "val_mask_dir = \"BraTS2020_TrainingData/input_data_128/val/masks/\"\n",
    "\n",
    "train_img_list=os.listdir(train_img_dir)\n",
    "train_mask_list = os.listdir(train_mask_dir)\n",
    "\n",
    "val_img_list=os.listdir(val_img_dir)\n",
    "val_mask_list = os.listdir(val_mask_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 2\n",
    "\n",
    "train_img_datagen = imageLoader(train_img_dir, train_img_list, \n",
    "                                train_mask_dir, train_mask_list, batch_size)\n",
    "\n",
    "val_img_datagen = imageLoader(val_img_dir, val_img_list, \n",
    "                                val_mask_dir, val_mask_list, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img, msk = train_img_datagen.__next__()\n",
    "\n",
    "img_num = random.randint(0,img.shape[0]-1)\n",
    "test_img=img[img_num]\n",
    "test_mask=msk[img_num]\n",
    "test_mask=np.argmax(test_mask, axis=3)\n",
    "\n",
    "n_slice=random.randint(0, test_mask.shape[2])\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.imshow(test_img[:,:,n_slice, 0], cmap='gray')\n",
    "plt.title('Image flair')\n",
    "plt.subplot(222)\n",
    "plt.imshow(test_img[:,:,n_slice, 1], cmap='gray')\n",
    "plt.title('Image t1ce')\n",
    "plt.subplot(223)\n",
    "plt.imshow(test_img[:,:,n_slice, 2], cmap='gray')\n",
    "plt.title('Image t2')\n",
    "plt.subplot(224)\n",
    "plt.imshow(test_mask[:,:,n_slice])\n",
    "plt.title('Mask')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iou(y_true, y_pred):\n",
    "    def f(y_true, y_pred):\n",
    "        intersection = (y_true * y_pred).sum()\n",
    "        union = y_true.sum() + y_pred.sum() - intersection\n",
    "        x = (intersection + 1e-15) / (union + 1e-15)\n",
    "        x = x.astype(np.float32)\n",
    "        return x\n",
    "\n",
    "    return tf.numpy_function(f, [y_true, y_pred], tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = [\"acc\", tf.keras.metrics.Recall(), tf.keras.metrics.Precision(), iou]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = 0.001\n",
    "optim = tf.keras.optimizers.Adam(LR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps_per_epoch = len(train_img_list)//batch_size\n",
    "val_steps_per_epoch = len(val_img_list)//batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = optim, loss=loss, metrics=metrics)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 128, 128, 1  0           []                               \n",
      "                                28, 3)]                                                           \n",
      "                                                                                                  \n",
      " conv3d (Conv3D)                (None, 128, 128, 12  1312        ['input_1[0][0]']                \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 128, 128, 12  0           ['conv3d[0][0]']                 \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " conv3d_1 (Conv3D)              (None, 128, 128, 12  6928        ['dropout[0][0]']                \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " max_pooling3d (MaxPooling3D)   (None, 64, 64, 64,   0           ['conv3d_1[0][0]']               \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_2 (Conv3D)              (None, 64, 64, 64,   13856       ['max_pooling3d[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 64, 64, 64,   0           ['conv3d_2[0][0]']               \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_3 (Conv3D)              (None, 64, 64, 64,   27680       ['dropout_1[0][0]']              \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " max_pooling3d_1 (MaxPooling3D)  (None, 32, 32, 32,   0          ['conv3d_3[0][0]']               \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_4 (Conv3D)              (None, 32, 32, 32,   55360       ['max_pooling3d_1[0][0]']        \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, 32, 32, 32,   0           ['conv3d_4[0][0]']               \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_5 (Conv3D)              (None, 32, 32, 32,   110656      ['dropout_2[0][0]']              \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " max_pooling3d_2 (MaxPooling3D)  (None, 16, 16, 16,   0          ['conv3d_5[0][0]']               \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_6 (Conv3D)              (None, 16, 16, 16,   221312      ['max_pooling3d_2[0][0]']        \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)            (None, 16, 16, 16,   0           ['conv3d_6[0][0]']               \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv3d_7 (Conv3D)              (None, 16, 16, 16,   442496      ['dropout_3[0][0]']              \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " max_pooling3d_3 (MaxPooling3D)  (None, 8, 8, 8, 128  0          ['conv3d_7[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv3d_8 (Conv3D)              (None, 8, 8, 8, 256  884992      ['max_pooling3d_3[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " dropout_4 (Dropout)            (None, 8, 8, 8, 256  0           ['conv3d_8[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv3d_9 (Conv3D)              (None, 8, 8, 8, 256  1769728     ['dropout_4[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv3d_transpose (Conv3DTransp  (None, 16, 16, 16,   262272     ['conv3d_9[0][0]']               \n",
      " ose)                           128)                                                              \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 16, 16, 16,   0           ['conv3d_transpose[0][0]',       \n",
      "                                256)                              'conv3d_7[0][0]']               \n",
      "                                                                                                  \n",
      " conv3d_10 (Conv3D)             (None, 16, 16, 16,   884864      ['concatenate[0][0]']            \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " dropout_5 (Dropout)            (None, 16, 16, 16,   0           ['conv3d_10[0][0]']              \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv3d_11 (Conv3D)             (None, 16, 16, 16,   442496      ['dropout_5[0][0]']              \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv3d_transpose_1 (Conv3DTran  (None, 32, 32, 32,   65600      ['conv3d_11[0][0]']              \n",
      " spose)                         64)                                                               \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 32, 32, 32,   0           ['conv3d_transpose_1[0][0]',     \n",
      "                                128)                              'conv3d_5[0][0]']               \n",
      "                                                                                                  \n",
      " conv3d_12 (Conv3D)             (None, 32, 32, 32,   221248      ['concatenate_1[0][0]']          \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " dropout_6 (Dropout)            (None, 32, 32, 32,   0           ['conv3d_12[0][0]']              \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_13 (Conv3D)             (None, 32, 32, 32,   110656      ['dropout_6[0][0]']              \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_transpose_2 (Conv3DTran  (None, 64, 64, 64,   16416      ['conv3d_13[0][0]']              \n",
      " spose)                         32)                                                               \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate)    (None, 64, 64, 64,   0           ['conv3d_transpose_2[0][0]',     \n",
      "                                64)                               'conv3d_3[0][0]']               \n",
      "                                                                                                  \n",
      " conv3d_14 (Conv3D)             (None, 64, 64, 64,   55328       ['concatenate_2[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " dropout_7 (Dropout)            (None, 64, 64, 64,   0           ['conv3d_14[0][0]']              \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_15 (Conv3D)             (None, 64, 64, 64,   27680       ['dropout_7[0][0]']              \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_transpose_3 (Conv3DTran  (None, 128, 128, 12  4112       ['conv3d_15[0][0]']              \n",
      " spose)                         8, 16)                                                            \n",
      "                                                                                                  \n",
      " concatenate_3 (Concatenate)    (None, 128, 128, 12  0           ['conv3d_transpose_3[0][0]',     \n",
      "                                8, 32)                            'conv3d_1[0][0]']               \n",
      "                                                                                                  \n",
      " conv3d_16 (Conv3D)             (None, 128, 128, 12  13840       ['concatenate_3[0][0]']          \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " dropout_8 (Dropout)            (None, 128, 128, 12  0           ['conv3d_16[0][0]']              \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " conv3d_17 (Conv3D)             (None, 128, 128, 12  6928        ['dropout_8[0][0]']              \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " conv3d_18 (Conv3D)             (None, 128, 128, 12  68          ['conv3d_17[0][0]']              \n",
      "                                8, 4)                                                             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 5,645,828\n",
      "Trainable params: 5,645,828\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, 128, 128, 128, 3)\n",
      "(None, 128, 128, 128, 4)\n",
      "129\n",
      "43\n"
     ]
    }
   ],
   "source": [
    "print(model.input_shape)\n",
    "print(model.output_shape)\n",
    "print(steps_per_epoch)\n",
    "print(val_steps_per_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "129/129 [==============================] - 129s 919ms/step - loss: 2.5538 - acc: 0.3409 - recall_1: 0.2761 - precision_1: 0.5459 - iou: 0.2442 - val_loss: 2.4562 - val_acc: 0.4452 - val_recall_1: 0.4236 - val_precision_1: 0.8395 - val_iou: 0.3402\n",
      "Epoch 2/100\n",
      "129/129 [==============================] - 131s 1s/step - loss: 2.2991 - acc: 0.4664 - recall_1: 0.4418 - precision_1: 0.6355 - iou: 0.3339 - val_loss: 2.4528 - val_acc: 0.5112 - val_recall_1: 0.4689 - val_precision_1: 0.8348 - val_iou: 0.3765\n",
      "Epoch 3/100\n",
      "129/129 [==============================] - 137s 1s/step - loss: 2.2941 - acc: 0.4906 - recall_1: 0.4598 - precision_1: 0.6512 - iou: 0.3459 - val_loss: 2.4325 - val_acc: 0.4681 - val_recall_1: 0.4369 - val_precision_1: 0.8722 - val_iou: 0.3492\n",
      "Epoch 4/100\n",
      "129/129 [==============================] - 140s 1s/step - loss: 2.2568 - acc: 0.4727 - recall_1: 0.4522 - precision_1: 0.6291 - iou: 0.3393 - val_loss: 2.5685 - val_acc: 0.4980 - val_recall_1: 0.4548 - val_precision_1: 0.8667 - val_iou: 0.3897\n",
      "Epoch 5/100\n",
      "129/129 [==============================] - 141s 1s/step - loss: 2.2609 - acc: 0.4739 - recall_1: 0.4520 - precision_1: 0.5812 - iou: 0.3387 - val_loss: 2.3787 - val_acc: 0.4546 - val_recall_1: 0.4378 - val_precision_1: 0.6245 - val_iou: 0.3287\n",
      "Epoch 6/100\n",
      "129/129 [==============================] - 141s 1s/step - loss: 2.2458 - acc: 0.4703 - recall_1: 0.4485 - precision_1: 0.5833 - iou: 0.3332 - val_loss: 2.4070 - val_acc: 0.5558 - val_recall_1: 0.4889 - val_precision_1: 0.7156 - val_iou: 0.3966\n",
      "Epoch 7/100\n",
      "129/129 [==============================] - 139s 1s/step - loss: 2.2199 - acc: 0.4968 - recall_1: 0.4658 - precision_1: 0.5965 - iou: 0.3480 - val_loss: 2.7670 - val_acc: 0.5219 - val_recall_1: 0.4949 - val_precision_1: 0.7082 - val_iou: 0.3991\n",
      "Epoch 8/100\n",
      "129/129 [==============================] - 142s 1s/step - loss: 2.2272 - acc: 0.4899 - recall_1: 0.4700 - precision_1: 0.5679 - iou: 0.3470 - val_loss: 2.3734 - val_acc: 0.4908 - val_recall_1: 0.4698 - val_precision_1: 0.6035 - val_iou: 0.3614\n",
      "Epoch 9/100\n",
      "129/129 [==============================] - 148s 1s/step - loss: 2.2104 - acc: 0.4972 - recall_1: 0.4669 - precision_1: 0.6071 - iou: 0.3478 - val_loss: 2.3455 - val_acc: 0.5842 - val_recall_1: 0.5015 - val_precision_1: 0.7151 - val_iou: 0.3922\n",
      "Epoch 10/100\n",
      "129/129 [==============================] - 151s 1s/step - loss: 2.1816 - acc: 0.5145 - recall_1: 0.4747 - precision_1: 0.6289 - iou: 0.3571 - val_loss: 2.3222 - val_acc: 0.4673 - val_recall_1: 0.4380 - val_precision_1: 0.6983 - val_iou: 0.3383\n",
      "Epoch 11/100\n",
      "129/129 [==============================] - 154s 1s/step - loss: 2.2134 - acc: 0.4735 - recall_1: 0.4467 - precision_1: 0.5959 - iou: 0.3386 - val_loss: 2.5063 - val_acc: 0.5408 - val_recall_1: 0.4574 - val_precision_1: 0.8153 - val_iou: 0.3997\n",
      "Epoch 12/100\n",
      "129/129 [==============================] - 149s 1s/step - loss: 2.2240 - acc: 0.4754 - recall_1: 0.4458 - precision_1: 0.5951 - iou: 0.3381 - val_loss: 2.4254 - val_acc: 0.5155 - val_recall_1: 0.4714 - val_precision_1: 0.7571 - val_iou: 0.3883\n",
      "Epoch 13/100\n",
      "129/129 [==============================] - 151s 1s/step - loss: 2.1833 - acc: 0.4914 - recall_1: 0.4605 - precision_1: 0.6203 - iou: 0.3507 - val_loss: 2.4911 - val_acc: 0.5031 - val_recall_1: 0.4659 - val_precision_1: 0.7315 - val_iou: 0.3733\n",
      "Epoch 14/100\n",
      "129/129 [==============================] - 153s 1s/step - loss: 2.1859 - acc: 0.4978 - recall_1: 0.4636 - precision_1: 0.5974 - iou: 0.3508 - val_loss: 2.3776 - val_acc: 0.5926 - val_recall_1: 0.4854 - val_precision_1: 0.7460 - val_iou: 0.3977\n",
      "Epoch 15/100\n",
      "129/129 [==============================] - 156s 1s/step - loss: 2.1645 - acc: 0.5024 - recall_1: 0.4646 - precision_1: 0.5996 - iou: 0.3544 - val_loss: 2.3829 - val_acc: 0.5300 - val_recall_1: 0.4924 - val_precision_1: 0.6887 - val_iou: 0.3863\n",
      "Epoch 16/100\n",
      "129/129 [==============================] - 156s 1s/step - loss: 2.1409 - acc: 0.4902 - recall_1: 0.4646 - precision_1: 0.5922 - iou: 0.3531 - val_loss: 2.2611 - val_acc: 0.5006 - val_recall_1: 0.4694 - val_precision_1: 0.6531 - val_iou: 0.3736\n",
      "Epoch 17/100\n",
      "129/129 [==============================] - 156s 1s/step - loss: 2.1301 - acc: 0.4968 - recall_1: 0.4616 - precision_1: 0.5898 - iou: 0.3568 - val_loss: 2.2849 - val_acc: 0.5534 - val_recall_1: 0.4899 - val_precision_1: 0.7157 - val_iou: 0.4029\n",
      "Epoch 18/100\n",
      "129/129 [==============================] - 155s 1s/step - loss: 2.1211 - acc: 0.5049 - recall_1: 0.4662 - precision_1: 0.5909 - iou: 0.3612 - val_loss: 2.2961 - val_acc: 0.5446 - val_recall_1: 0.4893 - val_precision_1: 0.6728 - val_iou: 0.4023\n",
      "Epoch 19/100\n",
      "129/129 [==============================] - 150s 1s/step - loss: 2.0751 - acc: 0.5141 - recall_1: 0.4778 - precision_1: 0.5828 - iou: 0.3686 - val_loss: 2.2784 - val_acc: 0.5571 - val_recall_1: 0.5167 - val_precision_1: 0.6096 - val_iou: 0.4147\n",
      "Epoch 20/100\n",
      "129/129 [==============================] - 153s 1s/step - loss: 2.0430 - acc: 0.5182 - recall_1: 0.4796 - precision_1: 0.5850 - iou: 0.3732 - val_loss: 2.2171 - val_acc: 0.5418 - val_recall_1: 0.5027 - val_precision_1: 0.6185 - val_iou: 0.3960\n",
      "Epoch 21/100\n",
      "129/129 [==============================] - 151s 1s/step - loss: 2.0324 - acc: 0.5196 - recall_1: 0.4852 - precision_1: 0.5768 - iou: 0.3748 - val_loss: 2.2130 - val_acc: 0.5641 - val_recall_1: 0.5157 - val_precision_1: 0.6652 - val_iou: 0.4187\n",
      "Epoch 22/100\n",
      "129/129 [==============================] - 157s 1s/step - loss: 2.0031 - acc: 0.5267 - recall_1: 0.4901 - precision_1: 0.5885 - iou: 0.3824 - val_loss: 2.1813 - val_acc: 0.5609 - val_recall_1: 0.5194 - val_precision_1: 0.6115 - val_iou: 0.4222\n",
      "Epoch 23/100\n",
      "129/129 [==============================] - 154s 1s/step - loss: 1.9921 - acc: 0.5258 - recall_1: 0.4900 - precision_1: 0.5768 - iou: 0.3806 - val_loss: 2.1324 - val_acc: 0.5489 - val_recall_1: 0.5163 - val_precision_1: 0.5973 - val_iou: 0.4131\n",
      "Epoch 24/100\n",
      "129/129 [==============================] - 153s 1s/step - loss: 1.9935 - acc: 0.5233 - recall_1: 0.4873 - precision_1: 0.5814 - iou: 0.3790 - val_loss: 2.0817 - val_acc: 0.5500 - val_recall_1: 0.5133 - val_precision_1: 0.6035 - val_iou: 0.4076\n",
      "Epoch 25/100\n",
      "129/129 [==============================] - 149s 1s/step - loss: 1.9449 - acc: 0.5291 - recall_1: 0.4974 - precision_1: 0.5876 - iou: 0.3878 - val_loss: 2.0674 - val_acc: 0.5432 - val_recall_1: 0.5124 - val_precision_1: 0.5863 - val_iou: 0.4073\n",
      "Epoch 26/100\n",
      "100/129 [======================>.......] - ETA: 29s - loss: 2.0474 - acc: 0.5286 - recall_1: 0.4974 - precision_1: 0.5688 - iou: 0.3836"
     ]
    }
   ],
   "source": [
    "history=model.fit(train_img_datagen,\n",
    "          steps_per_epoch=steps_per_epoch,\n",
    "          epochs=100,\n",
    "          verbose=1,\n",
    "          validation_data=val_img_datagen,\n",
    "          validation_steps=val_steps_per_epoch,\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('brats_3d.hdf5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "model=load_model('brats_3d.hdf5',custom_objects={'iou':iou})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_num = 82\n",
    "\n",
    "test_img = np.load(\"BraTS2020_TrainingData/input_data_128/val/images/image_\"+str(img_num)+\".npy\")\n",
    "\n",
    "test_mask = np.load(\"BraTS2020_TrainingData/input_data_128/val/masks/mask_\"+str(img_num)+\".npy\")\n",
    "test_mask_argmax=np.argmax(test_mask, axis=3)\n",
    "\n",
    "test_img_input = np.expand_dims(test_img, axis=0)\n",
    "test_prediction = model.predict(test_img_input)\n",
    "test_prediction_argmax=np.argmax(test_prediction, axis=4)[0,:,:,:]\n",
    "\n",
    "\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import random\n",
    "\n",
    "n_slice = 55\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(231)\n",
    "plt.title('Testing Image')\n",
    "plt.imshow(test_img[:,:,n_slice,1], cmap='gray')\n",
    "plt.subplot(232)\n",
    "plt.title('Testing Label')\n",
    "plt.imshow(test_mask_argmax[:,:,n_slice])\n",
    "plt.subplot(233)\n",
    "plt.title('Prediction on test image')\n",
    "plt.imshow(test_prediction_argmax[:,:, n_slice])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"AttentionResUNet\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_2 (InputLayer)           [(None, 128, 128, 1  0           []                               \n",
      "                                28, 3)]                                                           \n",
      "                                                                                                  \n",
      " conv3d_19 (Conv3D)             (None, 128, 128, 12  656         ['input_2[0][0]']                \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 128, 128, 12  32         ['conv3d_19[0][0]']              \n",
      " alization)                     8, 8)                                                             \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 128, 128, 12  0           ['batch_normalization[0][0]']    \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " conv3d_20 (Conv3D)             (None, 128, 128, 12  1736        ['activation[0][0]']             \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " conv3d_21 (Conv3D)             (None, 128, 128, 12  32          ['input_2[0][0]']                \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 128, 128, 12  32         ['conv3d_20[0][0]']              \n",
      " rmalization)                   8, 8)                                                             \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 128, 128, 12  32         ['conv3d_21[0][0]']              \n",
      " rmalization)                   8, 8)                                                             \n",
      "                                                                                                  \n",
      " dropout_9 (Dropout)            (None, 128, 128, 12  0           ['batch_normalization_1[0][0]']  \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " add (Add)                      (None, 128, 128, 12  0           ['batch_normalization_2[0][0]',  \n",
      "                                8, 8)                             'dropout_9[0][0]']              \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 128, 128, 12  0           ['add[0][0]']                    \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " max_pooling3d_4 (MaxPooling3D)  (None, 64, 64, 64,   0          ['activation_1[0][0]']           \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3d_22 (Conv3D)             (None, 64, 64, 64,   3472        ['max_pooling3d_4[0][0]']        \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 64, 64, 64,   64         ['conv3d_22[0][0]']              \n",
      " rmalization)                   16)                                                               \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 64, 64, 64,   0           ['batch_normalization_3[0][0]']  \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_23 (Conv3D)             (None, 64, 64, 64,   6928        ['activation_2[0][0]']           \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_24 (Conv3D)             (None, 64, 64, 64,   144         ['max_pooling3d_4[0][0]']        \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 64, 64, 64,   64         ['conv3d_23[0][0]']              \n",
      " rmalization)                   16)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 64, 64, 64,   64         ['conv3d_24[0][0]']              \n",
      " rmalization)                   16)                                                               \n",
      "                                                                                                  \n",
      " dropout_10 (Dropout)           (None, 64, 64, 64,   0           ['batch_normalization_4[0][0]']  \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " add_1 (Add)                    (None, 64, 64, 64,   0           ['batch_normalization_5[0][0]',  \n",
      "                                16)                               'dropout_10[0][0]']             \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 64, 64, 64,   0           ['add_1[0][0]']                  \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " max_pooling3d_5 (MaxPooling3D)  (None, 32, 32, 32,   0          ['activation_3[0][0]']           \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_25 (Conv3D)             (None, 32, 32, 32,   13856       ['max_pooling3d_5[0][0]']        \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 32, 32, 32,   128        ['conv3d_25[0][0]']              \n",
      " rmalization)                   32)                                                               \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 32, 32, 32,   0           ['batch_normalization_6[0][0]']  \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_26 (Conv3D)             (None, 32, 32, 32,   27680       ['activation_4[0][0]']           \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_27 (Conv3D)             (None, 32, 32, 32,   544         ['max_pooling3d_5[0][0]']        \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 32, 32, 32,   128        ['conv3d_26[0][0]']              \n",
      " rmalization)                   32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 32, 32, 32,   128        ['conv3d_27[0][0]']              \n",
      " rmalization)                   32)                                                               \n",
      "                                                                                                  \n",
      " dropout_11 (Dropout)           (None, 32, 32, 32,   0           ['batch_normalization_7[0][0]']  \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " add_2 (Add)                    (None, 32, 32, 32,   0           ['batch_normalization_8[0][0]',  \n",
      "                                32)                               'dropout_11[0][0]']             \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 32, 32, 32,   0           ['add_2[0][0]']                  \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " max_pooling3d_6 (MaxPooling3D)  (None, 16, 16, 16,   0          ['activation_5[0][0]']           \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_28 (Conv3D)             (None, 16, 16, 16,   55360       ['max_pooling3d_6[0][0]']        \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 16, 16, 16,   256        ['conv3d_28[0][0]']              \n",
      " rmalization)                   64)                                                               \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 16, 16, 16,   0           ['batch_normalization_9[0][0]']  \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_29 (Conv3D)             (None, 16, 16, 16,   110656      ['activation_6[0][0]']           \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_30 (Conv3D)             (None, 16, 16, 16,   2112        ['max_pooling3d_6[0][0]']        \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 16, 16, 16,   256        ['conv3d_29[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 16, 16, 16,   256        ['conv3d_30[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " dropout_12 (Dropout)           (None, 16, 16, 16,   0           ['batch_normalization_10[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " add_3 (Add)                    (None, 16, 16, 16,   0           ['batch_normalization_11[0][0]', \n",
      "                                64)                               'dropout_12[0][0]']             \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 16, 16, 16,   0           ['add_3[0][0]']                  \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " max_pooling3d_7 (MaxPooling3D)  (None, 8, 8, 8, 64)  0          ['activation_7[0][0]']           \n",
      "                                                                                                  \n",
      " conv3d_31 (Conv3D)             (None, 8, 8, 8, 128  221312      ['max_pooling3d_7[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 8, 8, 8, 128  512        ['conv3d_31[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 8, 8, 8, 128  0           ['batch_normalization_12[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv3d_32 (Conv3D)             (None, 8, 8, 8, 128  442496      ['activation_8[0][0]']           \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv3d_33 (Conv3D)             (None, 8, 8, 8, 128  8320        ['max_pooling3d_7[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 8, 8, 8, 128  512        ['conv3d_32[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_14 (BatchN  (None, 8, 8, 8, 128  512        ['conv3d_33[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " dropout_13 (Dropout)           (None, 8, 8, 8, 128  0           ['batch_normalization_13[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " add_4 (Add)                    (None, 8, 8, 8, 128  0           ['batch_normalization_14[0][0]', \n",
      "                                )                                 'dropout_13[0][0]']             \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 8, 8, 8, 128  0           ['add_4[0][0]']                  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv3d_34 (Conv3D)             (None, 8, 8, 8, 64)  8256        ['activation_9[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_15 (BatchN  (None, 8, 8, 8, 64)  256        ['conv3d_34[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 8, 8, 8, 64)  0           ['batch_normalization_15[0][0]'] \n",
      "                                                                                                  \n",
      " conv3d_36 (Conv3D)             (None, 8, 8, 8, 64)  4160        ['activation_10[0][0]']          \n",
      "                                                                                                  \n",
      " conv3d_transpose_4 (Conv3DTran  (None, 8, 8, 8, 64)  110656     ['conv3d_36[0][0]']              \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " conv3d_35 (Conv3D)             (None, 8, 8, 8, 64)  32832       ['activation_7[0][0]']           \n",
      "                                                                                                  \n",
      " add_5 (Add)                    (None, 8, 8, 8, 64)  0           ['conv3d_transpose_4[0][0]',     \n",
      "                                                                  'conv3d_35[0][0]']              \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 8, 8, 8, 64)  0           ['add_5[0][0]']                  \n",
      "                                                                                                  \n",
      " conv3d_37 (Conv3D)             (None, 8, 8, 8, 1)   65          ['activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 8, 8, 8, 1)   0           ['conv3d_37[0][0]']              \n",
      "                                                                                                  \n",
      " up_sampling3d (UpSampling3D)   (None, 16, 16, 16,   0           ['activation_12[0][0]']          \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      " lambda (Lambda)                (None, 16, 16, 16,   0           ['up_sampling3d[0][0]']          \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " multiply (Multiply)            (None, 16, 16, 16,   0           ['lambda[0][0]',                 \n",
      "                                64)                               'activation_7[0][0]']           \n",
      "                                                                                                  \n",
      " conv3d_38 (Conv3D)             (None, 16, 16, 16,   4160        ['multiply[0][0]']               \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " up_sampling3d_1 (UpSampling3D)  (None, 16, 16, 16,   0          ['activation_9[0][0]']           \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_16 (BatchN  (None, 16, 16, 16,   256        ['conv3d_38[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " concatenate_4 (Concatenate)    (None, 16, 16, 16,   0           ['up_sampling3d_1[0][0]',        \n",
      "                                192)                              'batch_normalization_16[0][0]'] \n",
      "                                                                                                  \n",
      " conv3d_39 (Conv3D)             (None, 16, 16, 16,   331840      ['concatenate_4[0][0]']          \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_17 (BatchN  (None, 16, 16, 16,   256        ['conv3d_39[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 16, 16, 16,   0           ['batch_normalization_17[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_40 (Conv3D)             (None, 16, 16, 16,   110656      ['activation_13[0][0]']          \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_41 (Conv3D)             (None, 16, 16, 16,   12352       ['concatenate_4[0][0]']          \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_18 (BatchN  (None, 16, 16, 16,   256        ['conv3d_40[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_19 (BatchN  (None, 16, 16, 16,   256        ['conv3d_41[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " dropout_14 (Dropout)           (None, 16, 16, 16,   0           ['batch_normalization_18[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " add_6 (Add)                    (None, 16, 16, 16,   0           ['batch_normalization_19[0][0]', \n",
      "                                64)                               'dropout_14[0][0]']             \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 16, 16, 16,   0           ['add_6[0][0]']                  \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv3d_42 (Conv3D)             (None, 16, 16, 16,   2080        ['activation_14[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_20 (BatchN  (None, 16, 16, 16,   128        ['conv3d_42[0][0]']              \n",
      " ormalization)                  32)                                                               \n",
      "                                                                                                  \n",
      " activation_15 (Activation)     (None, 16, 16, 16,   0           ['batch_normalization_20[0][0]'] \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_44 (Conv3D)             (None, 16, 16, 16,   1056        ['activation_15[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_transpose_5 (Conv3DTran  (None, 16, 16, 16,   27680      ['conv3d_44[0][0]']              \n",
      " spose)                         32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_43 (Conv3D)             (None, 16, 16, 16,   8224        ['activation_5[0][0]']           \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " add_7 (Add)                    (None, 16, 16, 16,   0           ['conv3d_transpose_5[0][0]',     \n",
      "                                32)                               'conv3d_43[0][0]']              \n",
      "                                                                                                  \n",
      " activation_16 (Activation)     (None, 16, 16, 16,   0           ['add_7[0][0]']                  \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_45 (Conv3D)             (None, 16, 16, 16,   33          ['activation_16[0][0]']          \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      " activation_17 (Activation)     (None, 16, 16, 16,   0           ['conv3d_45[0][0]']              \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      " up_sampling3d_2 (UpSampling3D)  (None, 32, 32, 32,   0          ['activation_17[0][0]']          \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      " lambda_1 (Lambda)              (None, 32, 32, 32,   0           ['up_sampling3d_2[0][0]']        \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " multiply_1 (Multiply)          (None, 32, 32, 32,   0           ['lambda_1[0][0]',               \n",
      "                                32)                               'activation_5[0][0]']           \n",
      "                                                                                                  \n",
      " conv3d_46 (Conv3D)             (None, 32, 32, 32,   1056        ['multiply_1[0][0]']             \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " up_sampling3d_3 (UpSampling3D)  (None, 32, 32, 32,   0          ['activation_14[0][0]']          \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_21 (BatchN  (None, 32, 32, 32,   128        ['conv3d_46[0][0]']              \n",
      " ormalization)                  32)                                                               \n",
      "                                                                                                  \n",
      " concatenate_5 (Concatenate)    (None, 32, 32, 32,   0           ['up_sampling3d_3[0][0]',        \n",
      "                                96)                               'batch_normalization_21[0][0]'] \n",
      "                                                                                                  \n",
      " conv3d_47 (Conv3D)             (None, 32, 32, 32,   82976       ['concatenate_5[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_22 (BatchN  (None, 32, 32, 32,   128        ['conv3d_47[0][0]']              \n",
      " ormalization)                  32)                                                               \n",
      "                                                                                                  \n",
      " activation_18 (Activation)     (None, 32, 32, 32,   0           ['batch_normalization_22[0][0]'] \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_48 (Conv3D)             (None, 32, 32, 32,   27680       ['activation_18[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_49 (Conv3D)             (None, 32, 32, 32,   3104        ['concatenate_5[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_23 (BatchN  (None, 32, 32, 32,   128        ['conv3d_48[0][0]']              \n",
      " ormalization)                  32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_24 (BatchN  (None, 32, 32, 32,   128        ['conv3d_49[0][0]']              \n",
      " ormalization)                  32)                                                               \n",
      "                                                                                                  \n",
      " dropout_15 (Dropout)           (None, 32, 32, 32,   0           ['batch_normalization_23[0][0]'] \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " add_8 (Add)                    (None, 32, 32, 32,   0           ['batch_normalization_24[0][0]', \n",
      "                                32)                               'dropout_15[0][0]']             \n",
      "                                                                                                  \n",
      " activation_19 (Activation)     (None, 32, 32, 32,   0           ['add_8[0][0]']                  \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv3d_50 (Conv3D)             (None, 32, 32, 32,   528         ['activation_19[0][0]']          \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_25 (BatchN  (None, 32, 32, 32,   64         ['conv3d_50[0][0]']              \n",
      " ormalization)                  16)                                                               \n",
      "                                                                                                  \n",
      " activation_20 (Activation)     (None, 32, 32, 32,   0           ['batch_normalization_25[0][0]'] \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_52 (Conv3D)             (None, 32, 32, 32,   272         ['activation_20[0][0]']          \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_transpose_6 (Conv3DTran  (None, 32, 32, 32,   6928       ['conv3d_52[0][0]']              \n",
      " spose)                         16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_51 (Conv3D)             (None, 32, 32, 32,   2064        ['activation_3[0][0]']           \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " add_9 (Add)                    (None, 32, 32, 32,   0           ['conv3d_transpose_6[0][0]',     \n",
      "                                16)                               'conv3d_51[0][0]']              \n",
      "                                                                                                  \n",
      " activation_21 (Activation)     (None, 32, 32, 32,   0           ['add_9[0][0]']                  \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_53 (Conv3D)             (None, 32, 32, 32,   17          ['activation_21[0][0]']          \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      " activation_22 (Activation)     (None, 32, 32, 32,   0           ['conv3d_53[0][0]']              \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      " up_sampling3d_4 (UpSampling3D)  (None, 64, 64, 64,   0          ['activation_22[0][0]']          \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      " lambda_2 (Lambda)              (None, 64, 64, 64,   0           ['up_sampling3d_4[0][0]']        \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " multiply_2 (Multiply)          (None, 64, 64, 64,   0           ['lambda_2[0][0]',               \n",
      "                                16)                               'activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " conv3d_54 (Conv3D)             (None, 64, 64, 64,   272         ['multiply_2[0][0]']             \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " up_sampling3d_5 (UpSampling3D)  (None, 64, 64, 64,   0          ['activation_19[0][0]']          \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_26 (BatchN  (None, 64, 64, 64,   64         ['conv3d_54[0][0]']              \n",
      " ormalization)                  16)                                                               \n",
      "                                                                                                  \n",
      " concatenate_6 (Concatenate)    (None, 64, 64, 64,   0           ['up_sampling3d_5[0][0]',        \n",
      "                                48)                               'batch_normalization_26[0][0]'] \n",
      "                                                                                                  \n",
      " conv3d_55 (Conv3D)             (None, 64, 64, 64,   20752       ['concatenate_6[0][0]']          \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_27 (BatchN  (None, 64, 64, 64,   64         ['conv3d_55[0][0]']              \n",
      " ormalization)                  16)                                                               \n",
      "                                                                                                  \n",
      " activation_23 (Activation)     (None, 64, 64, 64,   0           ['batch_normalization_27[0][0]'] \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_56 (Conv3D)             (None, 64, 64, 64,   6928        ['activation_23[0][0]']          \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_57 (Conv3D)             (None, 64, 64, 64,   784         ['concatenate_6[0][0]']          \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_28 (BatchN  (None, 64, 64, 64,   64         ['conv3d_56[0][0]']              \n",
      " ormalization)                  16)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_29 (BatchN  (None, 64, 64, 64,   64         ['conv3d_57[0][0]']              \n",
      " ormalization)                  16)                                                               \n",
      "                                                                                                  \n",
      " dropout_16 (Dropout)           (None, 64, 64, 64,   0           ['batch_normalization_28[0][0]'] \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " add_10 (Add)                   (None, 64, 64, 64,   0           ['batch_normalization_29[0][0]', \n",
      "                                16)                               'dropout_16[0][0]']             \n",
      "                                                                                                  \n",
      " activation_24 (Activation)     (None, 64, 64, 64,   0           ['add_10[0][0]']                 \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " conv3d_58 (Conv3D)             (None, 64, 64, 64,   136         ['activation_24[0][0]']          \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_30 (BatchN  (None, 64, 64, 64,   32         ['conv3d_58[0][0]']              \n",
      " ormalization)                  8)                                                                \n",
      "                                                                                                  \n",
      " activation_25 (Activation)     (None, 64, 64, 64,   0           ['batch_normalization_30[0][0]'] \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3d_60 (Conv3D)             (None, 64, 64, 64,   72          ['activation_25[0][0]']          \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3d_transpose_7 (Conv3DTran  (None, 64, 64, 64,   1736       ['conv3d_60[0][0]']              \n",
      " spose)                         8)                                                                \n",
      "                                                                                                  \n",
      " conv3d_59 (Conv3D)             (None, 64, 64, 64,   520         ['activation_1[0][0]']           \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " add_11 (Add)                   (None, 64, 64, 64,   0           ['conv3d_transpose_7[0][0]',     \n",
      "                                8)                                'conv3d_59[0][0]']              \n",
      "                                                                                                  \n",
      " activation_26 (Activation)     (None, 64, 64, 64,   0           ['add_11[0][0]']                 \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3d_61 (Conv3D)             (None, 64, 64, 64,   9           ['activation_26[0][0]']          \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      " activation_27 (Activation)     (None, 64, 64, 64,   0           ['conv3d_61[0][0]']              \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      " up_sampling3d_6 (UpSampling3D)  (None, 128, 128, 12  0          ['activation_27[0][0]']          \n",
      "                                8, 1)                                                             \n",
      "                                                                                                  \n",
      " lambda_3 (Lambda)              (None, 128, 128, 12  0           ['up_sampling3d_6[0][0]']        \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " multiply_3 (Multiply)          (None, 128, 128, 12  0           ['lambda_3[0][0]',               \n",
      "                                8, 8)                             'activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " conv3d_62 (Conv3D)             (None, 128, 128, 12  72          ['multiply_3[0][0]']             \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " up_sampling3d_7 (UpSampling3D)  (None, 128, 128, 12  0          ['activation_24[0][0]']          \n",
      "                                8, 16)                                                            \n",
      "                                                                                                  \n",
      " batch_normalization_31 (BatchN  (None, 128, 128, 12  32         ['conv3d_62[0][0]']              \n",
      " ormalization)                  8, 8)                                                             \n",
      "                                                                                                  \n",
      " concatenate_7 (Concatenate)    (None, 128, 128, 12  0           ['up_sampling3d_7[0][0]',        \n",
      "                                8, 24)                            'batch_normalization_31[0][0]'] \n",
      "                                                                                                  \n",
      " conv3d_63 (Conv3D)             (None, 128, 128, 12  5192        ['concatenate_7[0][0]']          \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " batch_normalization_32 (BatchN  (None, 128, 128, 12  32         ['conv3d_63[0][0]']              \n",
      " ormalization)                  8, 8)                                                             \n",
      "                                                                                                  \n",
      " activation_28 (Activation)     (None, 128, 128, 12  0           ['batch_normalization_32[0][0]'] \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " conv3d_64 (Conv3D)             (None, 128, 128, 12  1736        ['activation_28[0][0]']          \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " conv3d_65 (Conv3D)             (None, 128, 128, 12  200         ['concatenate_7[0][0]']          \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " batch_normalization_33 (BatchN  (None, 128, 128, 12  32         ['conv3d_64[0][0]']              \n",
      " ormalization)                  8, 8)                                                             \n",
      "                                                                                                  \n",
      " batch_normalization_34 (BatchN  (None, 128, 128, 12  32         ['conv3d_65[0][0]']              \n",
      " ormalization)                  8, 8)                                                             \n",
      "                                                                                                  \n",
      " dropout_17 (Dropout)           (None, 128, 128, 12  0           ['batch_normalization_33[0][0]'] \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " add_12 (Add)                   (None, 128, 128, 12  0           ['batch_normalization_34[0][0]', \n",
      "                                8, 8)                             'dropout_17[0][0]']             \n",
      "                                                                                                  \n",
      " activation_29 (Activation)     (None, 128, 128, 12  0           ['add_12[0][0]']                 \n",
      "                                8, 8)                                                             \n",
      "                                                                                                  \n",
      " conv3d_66 (Conv3D)             (None, 128, 128, 12  36          ['activation_29[0][0]']          \n",
      "                                8, 4)                                                             \n",
      "                                                                                                  \n",
      " batch_normalization_35 (BatchN  (None, 128, 128, 12  16         ['conv3d_66[0][0]']              \n",
      " ormalization)                  8, 4)                                                             \n",
      "                                                                                                  \n",
      " activation_30 (Activation)     (None, 128, 128, 12  0           ['batch_normalization_35[0][0]'] \n",
      "                                8, 4)                                                             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 1,717,816\n",
      "Trainable params: 1,715,120\n",
      "Non-trainable params: 2,696\n",
      "__________________________________________________________________________________________________\n",
      "(None, 128, 128, 128, 3)\n",
      "(None, 128, 128, 128, 4)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, BatchNormalization, Dropout, Lambda\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import models, layers, regularizers\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "\n",
    "metrics = [\"acc\", tf.keras.metrics.Recall(), tf.keras.metrics.Precision(), iou]\n",
    "def weighted_categorical_crossentropy(weights):\n",
    "    def wcce(y_true, y_pred):\n",
    "        Kweights = K.constant(weights)\n",
    "        y_true = K.cast(y_true, y_pred.dtype)\n",
    "        return K.categorical_crossentropy(y_true, y_pred) * K.sum(y_true * Kweights, axis=-1)\n",
    "    return wcce\n",
    "loss=weighted_categorical_crossentropy([0.25,54.34,54.34,27.97])\n",
    "from tensorflow.keras import models, layers, regularizers\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "def res_conv_block(x, filter_size, size, dropout, batch_norm=False):\n",
    "    conv = layers.Conv3D(size, (filter_size, filter_size, filter_size), padding='same')(x)\n",
    "    if batch_norm is True:\n",
    "        conv = layers.BatchNormalization(axis=4)(conv)\n",
    "    conv = layers.Activation('relu')(conv)\n",
    "    \n",
    "    conv = layers.Conv3D(size, (filter_size, filter_size, filter_size), padding='same')(conv)\n",
    "    if batch_norm is True:\n",
    "        conv = layers.BatchNormalization(axis=4)(conv)\n",
    "    #conv = layers.Activation('relu')(conv)    #Activation before addition with shortcut\n",
    "    if dropout > 0:\n",
    "        conv = layers.Dropout(dropout)(conv)\n",
    "\n",
    "    shortcut = layers.Conv3D(size, kernel_size=(1, 1, 1), padding='same')(x)\n",
    "    if batch_norm is True:\n",
    "        shortcut = layers.BatchNormalization(axis=4)(shortcut)\n",
    "\n",
    "    res_path = layers.add([shortcut, conv])\n",
    "    res_path = layers.Activation('relu')(res_path)    #Activation after addition with shortcut (Original residual block)\n",
    "    return res_path\n",
    "\n",
    "def gating_signal(input, out_size, batch_norm=False):\n",
    "    \"\"\"\n",
    "    resize the down layer feature map into the same dimension as the up layer feature map\n",
    "    using 1x1 conv\n",
    "    :return: the gating feature map with the same dimension of the up layer feature map\n",
    "    \"\"\"\n",
    "    x = layers.Conv3D(out_size, (1, 1, 1), padding='same')(input)\n",
    "    if batch_norm:\n",
    "        x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation('relu')(x)\n",
    "    return x\n",
    "\n",
    "def attention_block(x, gating, inter_shape):\n",
    "    shape_x = K.int_shape(x)\n",
    "    shape_g = K.int_shape(gating)\n",
    "\n",
    "# Getting the x signal to the same shape as the gating signal\n",
    "    theta_x = layers.Conv3D(inter_shape, (2, 2, 2), strides=(2, 2, 2), padding='same')(x)  # 16\n",
    "    shape_theta_x = K.int_shape(theta_x)\n",
    "\n",
    "# Getting the gating signal to the same number of filters as the inter_shape\n",
    "    phi_g = layers.Conv3D(inter_shape, (1, 1, 1), padding='same')(gating)\n",
    "    upsample_g = layers.Conv3DTranspose(inter_shape, (3, 3, 3),\n",
    "                                 strides=(shape_theta_x[1] // shape_g[1], shape_theta_x[2] // shape_g[2], shape_theta_x[3] // shape_g[3]),\n",
    "                                 padding='same')(phi_g)  # 16\n",
    "\n",
    "    concat_xg = layers.add([upsample_g, theta_x])\n",
    "    act_xg = layers.Activation('relu')(concat_xg)\n",
    "    psi = layers.Conv3D(1, (1, 1, 1), padding='same')(act_xg)\n",
    "    sigmoid_xg = layers.Activation('softmax')(psi)\n",
    "    shape_sigmoid = K.int_shape(sigmoid_xg)\n",
    "    upsample_psi = layers.UpSampling3D(size=(shape_x[1] // shape_sigmoid[1], shape_x[2] // shape_sigmoid[2], shape_x[3] // shape_sigmoid[3]))(sigmoid_xg)  # 32\n",
    "\n",
    "    upsample_psi = repeat_elem(upsample_psi, shape_x[4])\n",
    "\n",
    "    y = layers.multiply([upsample_psi, x])\n",
    "\n",
    "    result = layers.Conv3D(shape_x[4], (1, 1, 1), padding='same')(y)\n",
    "    result_bn = layers.BatchNormalization()(result)\n",
    "    return result_bn\n",
    "\n",
    "\n",
    "\n",
    "def repeat_elem(tensor, rep):\n",
    "    # lambda function to repeat Repeats the elements of a tensor along an axis\n",
    "    #by a factor of rep.\n",
    "    # If tensor has shape (None, 256,256,3), lambda will return a tensor of shape \n",
    "    #(None, 256,256,6), if specified axis=3 and rep=2.\n",
    "\n",
    "     return layers.Lambda(lambda x, repnum: K.repeat_elements(x, repnum, axis=4),\n",
    "                          arguments={'repnum': rep})(tensor)\n",
    "\n",
    "\n",
    "\n",
    "def Attention_ResUNet(input_shape, NUM_CLASSES, dropout_rate=0.2, batch_norm=True):\n",
    "    '''\n",
    "    Rsidual UNet, with attention \n",
    "    \n",
    "    '''\n",
    "    # network structure\n",
    "    FILTER_NUM = 8\n",
    "    FILTER_SIZE = 3 # size of the convolutional filter\n",
    "    UP_SAMP_SIZE = 2 # size of upsampling filters\n",
    "    # input data\n",
    "    # dimension of the image depth\n",
    "    inputs = layers.Input(input_shape, dtype=tf.float32)\n",
    "    axis = 4\n",
    "\n",
    "    # Downsampling layers\n",
    "    # DownRes 1, double residual convolution + pooling\n",
    "    conv_128 = res_conv_block(inputs, FILTER_SIZE, FILTER_NUM, dropout_rate, batch_norm)\n",
    "    pool_64 = layers.MaxPooling3D(pool_size=(2,2,2))(conv_128)\n",
    "    # DownRes 2\n",
    "    conv_64 = res_conv_block(pool_64, FILTER_SIZE, 2*FILTER_NUM, dropout_rate, batch_norm)\n",
    "    pool_32 = layers.MaxPooling3D(pool_size=(2,2,2))(conv_64)\n",
    "    # DownRes 3\n",
    "    conv_32 = res_conv_block(pool_32, FILTER_SIZE, 4*FILTER_NUM, dropout_rate, batch_norm)\n",
    "    pool_16 = layers.MaxPooling3D(pool_size=(2,2,2))(conv_32)\n",
    "    # DownRes 4\n",
    "    conv_16 = res_conv_block(pool_16, FILTER_SIZE, 8*FILTER_NUM, dropout_rate, batch_norm)\n",
    "    pool_8 = layers.MaxPooling3D(pool_size=(2,2,2))(conv_16)\n",
    "    # DownRes 5, convolution only\n",
    "    conv_8 = res_conv_block(pool_8, FILTER_SIZE, 16*FILTER_NUM, dropout_rate, batch_norm)\n",
    "\n",
    "    # Upsampling layers\n",
    "    # UpRes 6, attention gated concatenation + upsampling + double residual convolution\n",
    "    gating_16 = gating_signal(conv_8, 8*FILTER_NUM, batch_norm)\n",
    "    att_16 = attention_block(conv_16, gating_16, 8*FILTER_NUM)\n",
    "    up_16 = layers.UpSampling3D(size=(UP_SAMP_SIZE, UP_SAMP_SIZE, UP_SAMP_SIZE), data_format=\"channels_last\")(conv_8)\n",
    "    up_16 = layers.concatenate([up_16, att_16 ], axis=axis)\n",
    "    up_conv_16 = res_conv_block(up_16, FILTER_SIZE, 8*FILTER_NUM, dropout_rate, batch_norm)\n",
    "    # UpRes 7\n",
    "    gating_32 = gating_signal(up_conv_16, 4*FILTER_NUM, batch_norm)\n",
    "    att_32 = attention_block(conv_32, gating_32, 4*FILTER_NUM)\n",
    "    up_32 = layers.UpSampling3D(size=(UP_SAMP_SIZE, UP_SAMP_SIZE, UP_SAMP_SIZE), data_format=\"channels_last\")(up_conv_16)\n",
    "    up_32 = layers.concatenate([up_32, att_32], axis=axis)\n",
    "    up_conv_32 = res_conv_block(up_32, FILTER_SIZE, 4*FILTER_NUM, dropout_rate, batch_norm)\n",
    "    # UpRes 8\n",
    "    gating_64 = gating_signal(up_conv_32, 2*FILTER_NUM, batch_norm)\n",
    "    att_64 = attention_block(conv_64, gating_64, 2*FILTER_NUM)\n",
    "    up_64 = layers.UpSampling3D(size=(UP_SAMP_SIZE, UP_SAMP_SIZE, UP_SAMP_SIZE), data_format=\"channels_last\")(up_conv_32)\n",
    "    up_64 = layers.concatenate([up_64, att_64], axis=axis)\n",
    "    up_conv_64 = res_conv_block(up_64, FILTER_SIZE, 2*FILTER_NUM, dropout_rate, batch_norm)\n",
    "    # UpRes 9\n",
    "    gating_128 = gating_signal(up_conv_64, FILTER_NUM, batch_norm)\n",
    "    att_128 = attention_block(conv_128, gating_128, FILTER_NUM)\n",
    "    up_128 = layers.UpSampling3D(size=(UP_SAMP_SIZE, UP_SAMP_SIZE, UP_SAMP_SIZE), data_format=\"channels_last\")(up_conv_64)\n",
    "    up_128 = layers.concatenate([up_128, att_128], axis=axis)\n",
    "    up_conv_128 = res_conv_block(up_128, FILTER_SIZE, FILTER_NUM, dropout_rate, batch_norm)\n",
    "\n",
    "    # 1*1 convolutional layers\n",
    "    \n",
    "    conv_final = layers.Conv3D(NUM_CLASSES, kernel_size=(1,1,1))(up_conv_128)\n",
    "    conv_final = layers.BatchNormalization(axis=axis)(conv_final)\n",
    "    conv_final = layers.Activation('softmax')(conv_final)  #Change to softmax for multichannel\n",
    "\n",
    "    # Model integration\n",
    "    model = models.Model(inputs, conv_final, name=\"AttentionResUNet\")\n",
    "    return model\n",
    "model2=Attention_ResUNet(input_shape=(128,128,128,3), NUM_CLASSES=4, dropout_rate=0.3, batch_norm=True)\n",
    "model2.compile(optimizer = 'adam', loss=loss, metrics=metrics)\n",
    "model2.summary()\n",
    "print(model2.input_shape)\n",
    "print(model2.output_shape)\n",
    "steps_per_epoch=len(train_img_list)//batch_size\n",
    "val_steps_per_epoch=len(val_img_list)//batch_size\n",
    "history=model2.fit(train_img_datagen,\n",
    "          steps_per_epoch=steps_per_epoch,\n",
    "          epochs=100,\n",
    "          verbose=1,\n",
    "          validation_data=val_img_datagen,\n",
    "          validation_steps=val_steps_per_epoch,\n",
    "          )\n",
    "model2.save('brats_3d_attention.hdf5')\n",
    "\n",
    "\n",
    "# img_num=82\n",
    "# test_img = np.load(\"BraTS2020_TrainingData/input_data_128/val/images/image_\"+str(img_num)+\".npy\")\n",
    "# test_mask = np.load(\"BraTS2020_TrainingData/input_data_128/val/masks/mask_\"+str(img_num)+\".npy\")\n",
    "\n",
    "# test_mask_argmax = np.argmax(test_mask, axis=3)\n",
    "# test_img_input = np.expand_dims(test_img, axis=0)\n",
    "# test_prediction = model2.predict(test_img_input)\n",
    "# test_prediction_argmax = np.argmax(test_prediction, axis=4)[0,:,:,:]\n",
    "\n",
    "# from matplotlib import pyplot as plt\n",
    "# import random\n",
    "\n",
    "# n_slice = 55\n",
    "# plt.figure(figsize=(12,8))\n",
    "# plt.subplot(231)\n",
    "# plt.title('Testing Image')\n",
    "# plt.imsave(\"imageplot_attent.png\",test_img[:,:,n_slice,1])\n",
    "# plt.subplot(232)\n",
    "# plt.title('Testing Label')\n",
    "# plt.imsave(\"labelplot_attent.png\",test_mask_argmax[:,:,n_slice])\n",
    "# plt.subplot(233)\n",
    "# plt.title('Prediction on test image')\n",
    "# plt.imsave(\"predimage_attent.png\",test_prediction_argmax[:,:,n_slice])\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weighted_categorical_crossentropy(weights):\n",
    "    def wcce(y_true, y_pred):\n",
    "        Kweights = K.constant(weights)\n",
    "        y_true = K.cast(y_true, y_pred.dtype)\n",
    "        return K.categorical_crossentropy(y_true, y_pred) * K.sum(y_true * Kweights, axis=-1)\n",
    "    return wcce\n",
    "loss=weighted_categorical_crossentropy([0.25,54.34,54.34,27.97])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=load_model('brats_3d_attention.hdf5',custom_objects={'iou':iou,'wcce':loss})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_num = 82\n",
    "\n",
    "test_img = np.load(\"BraTS2020_TrainingData/input_data_128/val/images/image_\"+str(img_num)+\".npy\")\n",
    "\n",
    "test_mask = np.load(\"BraTS2020_TrainingData/input_data_128/val/masks/mask_\"+str(img_num)+\".npy\")\n",
    "test_mask_argmax=np.argmax(test_mask, axis=3)\n",
    "\n",
    "test_img_input = np.expand_dims(test_img, axis=0)\n",
    "test_prediction = model2.predict(test_img_input)\n",
    "test_prediction_argmax=np.argmax(test_prediction, axis=4)[0,:,:,:]\n",
    "\n",
    "\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import random\n",
    "\n",
    "n_slice = 55\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(231)\n",
    "plt.title('Testing Image')\n",
    "plt.imshow(test_img[:,:,n_slice,1], cmap='gray')\n",
    "plt.subplot(232)\n",
    "plt.title('Testing Label')\n",
    "plt.imshow(test_mask_argmax[:,:,n_slice])\n",
    "plt.subplot(233)\n",
    "plt.title('Prediction on test image')\n",
    "plt.imshow(test_prediction_argmax[:,:, n_slice])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "References:\n",
    "\n",
    "1) https://github.com/bnsreenu/python_for_microscopists/tree/master/231_234_BraTa2020_Unet_segmentation\n",
    "\n",
    "\n",
    "2) https://github.com/bnsreenu/python_for_microscopists/blob/master/224_225_226_models.py\n",
    "\n",
    "\n",
    "3) https://stackoverflow.com/questions/51361402/weighted-categorical-cross-entropy-semantic-segmentation\n",
    "\n",
    "\n",
    "4) We have utilized IU Carbonate servers to train our models. \n",
    "\n",
    "5) Dataset can be downloaded from the following link: https://www.kaggle.com/awsaf49/brats20-dataset-training-validation\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "82ce0d89ee0bef264e5a5667810de224121ca3a5b737b64db508885920a58418"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}