A command line tool to transform a DICOM volume into a 3d surface mesh (obj, stl or ply). Several mesh processing routines can be enabled, such as mesh reduction, smoothing or cleaning. Works on Linux, OSX and Windows.

COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. A Flask App was later developed wherein user can…

An official implementation of PCRLv2 (pre-training and fine-tuning code are included).

AiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charita…

Image-based COVID-19 diagnosis. Links to software, data, and other resources.

Fully automated code for Covid-19 detection from CT scans from paper: https://doi.org/10.1016/j.bspc.2021.102588

End-to-end Python CT volume preprocessing pipeline to convert raw DICOMs into clean 3D numpy arrays for ML. From paper Draelos et al. "Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes."

🔀 Medical software for Processing multi-Parametric images Pipelines

A simple privacy-focused web panel in flask for labeling CT Scan's slices

Segmentation and Classification models for COVID CT scans (COVID, pneumonia, normal) based on Mask R-CNN.

Machine learning models for multi-organ, multi-disease prediction in chest CT volumes. From paper Draelos et al. "Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes."

CNN's for bone segmentation of CT-scans.

A COVID-19 CT Scan Dataset Applicable in Machine Learning and Deep Learning

CTSegNet is an end-to-end 3D segmentation package for large X-ray tomographic datasets using 2D fully convolutional neural networks (fCNN).

Deep CNN for performing 3D super resolution on CT/MRI scans

Idiopathic pulmonary fibrosis (IPF) is a restrictive interstitial lung disease that causes lung function decline by lung tissue scarring. Although lung function decline is assessed by the forced vital capacity (FVC), determining the accurate progression of IPF remains a challenge. To address this challenge, we proposed Fibro-CoSANet, a novel end…

A Unity scene setup that generates a 3D Texture from a series of CT scans and turn it into a volume of particles

PyTorch implementation of 3D U-Net for kidney and tumor segmentation from KiTS19 CT scans.

View volumetric (3D) medical images in Jupyter notebooks