A 3D multi-modal medical image segmentation library in PyTorch

Our goal is to implementent an open-source medical image segmentation library of state of the art 3D deep neural networks in PyTorch along with data loaders of the most common medical MRI datasets. The first stable release of our repository is almost ready. We strongly believe in open and reproducible deep learning research. This project started as an MSc Thesis and is currently under further development.

Beta release - work in progress!

Although this work was initially focused on 3D multi-modal brain MRI segmentation we are slowly adding more architectures and dataloaders. Stay tuned!

Quick Start

If you want to quickly understand the fundamental concepts we strongly advice to check our blog post, which provides a high level overview of all the aspects of medical image segmentation and deep learning.
Alternatively, you can create a virtual environment and install the requirements. Check installation folder for more instructions.

Implemented architectures

U-Net3D Learning Dense Volumetric Segmentation from Sparse Annotation Learning Dense Volumetric Segmentation from Sparse Annotation

V-net Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

ResNet3D-VAE 3D MRI brain tumor segmentation using autoencoder regularization

U-Net Convolutional Networks for Biomedical Image Segmentation

COVID-Net A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images

SkipDesneNet3D 3D Densely Convolutional Networks for Volumetric Segmentation

HyperDense-Net A hyper-densely connected CNN for multi-modal image segmentation

multi-stream Densenet3D A hyper-densely connected CNN for multi-modal image segmentation

DenseVoxelNet Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

Implemented dataloaders

Iseg 2017

Mrbrains 2018

MICCAI Gleason 2019 Challenge

MICCAI BraTs2018

IXI brain development Dataset

Results

To be updated really really soon......(this month)

Usage

How to train your model

For Iseg-2017 :

python ./tests/train_iseg.py --args

For MR brains 2018 (4 classes)

python ./tests/train_mrbrains_4_classes.py --args

For MR brains 2018 (8 classes)

python ./tests/train_mrbrains_8_classes.py --args

For MICCAI 2019 Gleason Challenge

python ./tests/test_miccai_2019.py --args

Arguments that you can modify

--batchSz, type=int, default=4, help='The batch size for training and validation'

--dim,  default=(64, 64, 64),  help='The sub-image or sub-volume that you want to crop for 2D specify as dim=(64, 64)' 

--nEpochs, type=int, default=250 ,  help='The training epochs'

--inChannels, type=int, choices=(1,2,3) , help='The desired modalities/channels that you want to use'

--inModalities, type=int, choices=(1,2,3), help='The modalities of the dataset'

--samples_train, type=int, default=10

--samples_val, type=int, default=10

--fold_id, default='1', type=str, help='Select subject for fold validation'

--lr, default=1e-3, type=float, help='learning rate (default: 1e-3)'

--cuda, default=True, help='whether you want to use cuda'

--model, type=str, default='UNET3D', choices=("RESNET3DVAE",'UNET3D',  'DENSENET1', 'DENSENET2', 'DENSENET3', 'HYPERDENSENET', "SKIPDENSENET3D",
                  "DENSEVOXELNET",'VNET','VNET2')

 --opt', type=str, default='sgd', choices=('sgd', 'adam', 'rmsprop')

New released cool features (04/2020)

On the fly 3D total volume visualization
Tensorboard and PyTorch 1.4 support to track training progress
Code cleanup and packages creation
Offline sub-volume generation
Add Hyperdensenet, 3DResnet-VAE, DenseVoxelNet
Fix mrbrains,Brats2018, IXI,MICCAI 2019 gleason challenge dataloaders
Add confusion matrix support for understanding training dynamics
Write Tests for the project

Top priorities

Unify/Generalize Train and Test functions
Test new architectures
Minimal test pred example with pretrained models
Save produced 3d-total-segmenentation as nifti files

Current team

Ilias Papastatis

Nikolas Adaloglou

License and citation

Advice the LICENSE.md file. For usage of third party libraries and repositories please advise the respective distributed terms. It would be nice to cite the original models and datasets. If you want, you can also cite this work as:

@MastersThesis{adaloglou2019MRIsegmentation,
author = {Adaloglou Nikolaos},
title={Deep learning in medical image analysis: a comparative analysis of
multi-modal brain-MRI segmentation with 3D deep neural networks},
school = {University of Patras},
note="\url{https://github.com/black0017/MedicalZooPytorch}",
year = {2019},
organization={Nemertes}}

Support

If you really like this repository and find it useful, please consider (★) starring it, so that it can reach a broader audience of like-minded people. It would be highly appreciated :) !

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