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

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We strongly believe in open and reproducible deep learning research. Our goal is to implement an open-source medical image segmentation library of state of the art 3D deep neural networks in PyTorch. We also implemented a bunch of data loaders of the most common medical image datasets. This project started as an MSc Thesis and is currently under further development. Although this work was initially focused on 3D multi-modal brain MRI segmentation we are slowly adding more architectures and data-loaders.

Top priorities 21-07

[Update] 21-07 We have just received a brand new GPU. The project developedment was postponed due to lack of computational resources. We will be back with more updates. Please Watch our Github repository for releases to be notified. We are always looking for passionate open-source contributos. Full credits will be given.

Quick Start

Implemented architectures

Implemented medical imaging data-loaders

TaskData Info/ ModalitiesTrain/TestVolume sizeClassesDataset size (GB)
Iseg 2017T1, T210 / 10144x192x25640.72
Iseg 2019T1, T210 / 13144x192x25640.75
MICCAI BraTs2018FLAIR, T1w, T1gd,T2w285 / -240x240x1559 or 42.4
MICCAI BraTs2019FLAIR, T1w, T1gd,T2w335 / 125240x240x1559 or 44
Mrbrains 2018FLAIR, T1w, T1gd,T2w8240x240x489 or 40.5
IXI brain development Dataset T1,T2 no labels581(110~150)x256x256-8.7
MICCAI Gleason 2019 Challenge2D pathology images~2505K x 5K-2.5

Preliminary results

Visual results on Iseg-2017

<img src="figures/comparison.png"/>

Iseg and Mr-brains

Model# Params (M)MACS(G)Iseg 2017 DSC (%)Mr-brains 4 classes DSC (%)
Unet3D17 M0.993.8488.61
Vnet45 M1287.2184.09
DenseNet3D3 M5.181.6579.85
SkipDenseNet3D1.5 M31--
DenseVoxelNet1.8 M8--
HyperDenseNet10.4 M5.8--

Usage

How to train your model

python ./examples/train_iseg2017_new.py --args
python ./examples/train_mrbrains_4_classes.py --args
python ./examples/train_mrbrains_9_classes.py --args
python ./examples/test_miccai_2019.py --args

Inference

How to test your trained model in a medical image

python ./tests/inference.py --args

Covid-19 segmentation and classification

We provide some implementations around Covid-19 for humanitarian purposes. In detail:

Classification model

Datasets

Classification from 2D images:

3D COVID-19 segmentation dataset

Latest features (06/2020)

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 :) !

Contributing to Medical ZOO

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues. More info on the contribute directory.

Current team

Ilias Papastatis, Sergios Karagianakos and Nikolas Adaloglou

License , citation and acknowledgements

Please 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}}

Acknowledgements

In general, in the open source community recognizing third party utilities increases the credibility of your software. In deep learning, academics tend to skip acknowledging third party repos for some reason. In essence, we used whatever resource we needed to make this project self-complete, that was nicely written. However, modifications were performed to match the project structure and requirements. Here is the list of the top-based works: HyperDenseNet model. Most of the segmentation losses from here. 3D-SkipDenseNet model from here. 3D-ResNet base model from here. Abstract model class from MimiCry project. Trainer and Writer class from PyTorch template. Covid-19 implementation based on our previous work from here. MICCAI 2019 Gleason challenge data-loaders based on our previous work from here. Basic 2D Unet implementation from here.Vnet model from here