Implement nuclei segmentation using Voronoi-Otsu-Labeling (Issue #7)

nuclei_segmentation.ipynb

@@ -0,0 +1,175 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nuclei Segmentation using Voronoi-Otsu-Labeling\n",
+    "\n",
+    "This notebook demonstrates how to segment nuclei in an image using Voronoi-Otsu-Labeling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-13T11:32:07.343272Z",
+     "iopub.status.busy": "2024-10-13T11:32:07.343095Z",
+     "iopub.status.idle": "2024-10-13T11:32:08.667576Z",
+     "shell.execute_reply": "2024-10-13T11:32:08.666917Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/napari_tools_menu/__init__.py:10: UserWarning: Importing QT failed; now introducing dummy definitions of QMenu class and register_function decorator.\n",
+      "  warnings.warn(\"Importing QT failed; now introducing dummy definitions of QMenu class and register_function decorator.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import napari_segment_blobs_and_things_with_membranes as nsbatwm\n",
+    "from skimage.io import imread, imsave\n",
+    "import stackview\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-13T11:32:08.703546Z",
+     "iopub.status.busy": "2024-10-13T11:32:08.702936Z",
+     "iopub.status.idle": "2024-10-13T11:32:08.712596Z",
+     "shell.execute_reply": "2024-10-13T11:32:08.712093Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "image = imread('human_mitosis_small.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Segment nuclei using Voronoi-Otsu-Labeling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-13T11:32:08.714401Z",
+     "iopub.status.busy": "2024-10-13T11:32:08.714218Z",
+     "iopub.status.idle": "2024-10-13T11:32:10.126286Z",
+     "shell.execute_reply": "2024-10-13T11:32:10.125725Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/napari_segment_blobs_and_things_with_membranes/__init__.py:564: UserWarning: threshold_otsu is expected to work correctly only for grayscale images; image shape (180, 247, 4) looks like that of an RGB image.\n",
+      "  threshold = sk_threshold_otsu(blurred_outline)\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = nsbatwm.voronoi_otsu_labeling(image, spot_sigma=3.5, outline_sigma=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-13T11:32:10.130002Z",
+     "iopub.status.busy": "2024-10-13T11:32:10.128027Z",
+     "iopub.status.idle": "2024-10-13T11:32:10.473470Z",
+     "shell.execute_reply": "2024-10-13T11:32:10.472662Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "index 192 is out of bounds for axis 0 with size 180",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mstackview\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manimate_curtain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msegmentation_result.gif\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/stackview/_animate.py:140\u001b[0m, in \u001b[0;36manimate_curtain\u001b[0;34m(timelapse, timelapse_curtain, axis, alpha, num_steps, zoom_factor, colormap, display_min, display_max, curtain_colormap, curtain_display_min, curtain_display_max, filename, overwrite_file, frame_delay_ms, num_loops)\u001b[0m\n\u001b[1;32m    137\u001b[0m     image_slice_curtain \u001b[38;5;241m=\u001b[39m _img_to_rgb(timelapse_curtain, colormap\u001b[38;5;241m=\u001b[39mcurtain_colormap, display_min\u001b[38;5;241m=\u001b[39mcurtain_display_min,\n\u001b[1;32m    138\u001b[0m                                       display_max\u001b[38;5;241m=\u001b[39mcurtain_display_max)\n\u001b[1;32m    139\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 140\u001b[0m     image_slice \u001b[38;5;241m=\u001b[39m _img_to_rgb(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimelapse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslider_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m, colormap\u001b[38;5;241m=\u001b[39mcolormap,\n\u001b[1;32m    141\u001b[0m                               display_min\u001b[38;5;241m=\u001b[39mdisplay_min, display_max\u001b[38;5;241m=\u001b[39mdisplay_max)\n\u001b[1;32m    142\u001b[0m     image_slice_curtain \u001b[38;5;241m=\u001b[39m _img_to_rgb(np\u001b[38;5;241m.\u001b[39mtake(timelapse_curtain, slider_value, axis\u001b[38;5;241m=\u001b[39maxis),\n\u001b[1;32m    143\u001b[0m                                       colormap\u001b[38;5;241m=\u001b[39mcurtain_colormap, display_min\u001b[38;5;241m=\u001b[39mcurtain_display_min,\n\u001b[1;32m    144\u001b[0m                                       display_max\u001b[38;5;241m=\u001b[39mcurtain_display_max)\n\u001b[1;32m    145\u001b[0m image_slice[:, slider_value:] \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m alpha) \u001b[38;5;241m*\u001b[39m image_slice[:, slider_value:] \u001b[38;5;241m+\u001b[39m \\\n\u001b[1;32m    146\u001b[0m                                         alpha \u001b[38;5;241m*\u001b[39m image_slice_curtain[:, slider_value:]\n",
+      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:207\u001b[0m, in \u001b[0;36mtake\u001b[0;34m(a, indices, axis, out, mode)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_take_dispatcher)\n\u001b[1;32m    110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtake\u001b[39m(a, indices, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m    111\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;124;03m    Take elements from an array along an axis.\u001b[39;00m\n\u001b[1;32m    113\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[38;5;124;03m           [5, 7]])\u001b[39;00m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 207\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtake\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _wrapit(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 57\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbound\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m     59\u001b[0m     \u001b[38;5;66;03m# A TypeError occurs if the object does have such a method in its\u001b[39;00m\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;66;03m# class, but its signature is not identical to that of NumPy's. This\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[38;5;66;03m# Call _wrapit from within the except clause to ensure a potential\u001b[39;00m\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;66;03m# exception has a traceback chain.\u001b[39;00m\n\u001b[1;32m     66\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _wrapit(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n",
+      "\u001b[0;31mIndexError\u001b[0m: index 192 is out of bounds for axis 0 with size 180"
+     ]
+    }
+   ],
+   "source": [
+    "stackview.animate_curtain(image, labels, filename=\"segmentation_result.gif\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save the label image as RGB PNG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skimage import color\n",
+    "rgb_labels = color.label2rgb(labels, bg_label=0)\n",
+    "imsave(\"segmented_nuclei.png\", (rgb_labels * 255).astype(np.uint8))"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

requirements.txt

@@ -10,4 +10,4 @@ scipy
 napari-segment-blobs-and-things-with-membranes
 napari-simpleitk-image-processing
 opencv-python
-
+jupyter