MedicalZooPytorch：基于pytorch的深度学习框架，用于多模式2D / 3D医学图像分割

MedicalZooPytorch：基于pytorch的深度学习框架，用于多模式2D / 3D医学图像分割

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

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. We are currently working toward a stable beta release. Please Watch our Github repository for releases to be notified. Stay tuned! More updates are coming...

Quick Start

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 auto-encoder regularization U-Net Convolutional Networks for Biomedical Image Segmentation 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 MED3D Transfer learning for 3D medical image analysis HighResNet3D On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

Implemented medical imaging data-loaders

Preliminary results

Visual results on Iseg-2017

Iseg and Mr-brains

Usage

How to train your model

For Iseg-2017 :

python ./examples/train_iseg2017_new.py --args

For MR brains 2018 (4 classes)

python ./examples/train_mrbrains_4_classes.py --args

For MR brains 2018 (8 classes)

python ./examples/train_mrbrains_9_classes.py --args

For MICCAI 2019 Gleason Challenge

python ./examples/test_miccai_2019.py --args

The arguments that you can modify are extensively listed in the manual.

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

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

Datasets

Classification from 2D images:

COVID-CT dataset COVIDx dataset

3D COVID-19 segmentation dataset

COVID-19 CT Lung and Infection Segmentation Dataset

New released cool features (05/2020)

On the fly 3D total volume visualizationTensorboard and PyTorch 1.4+ support to track training progressCode cleanup and packages creationOffline sub-volume generationAdd Hyperdensenet, 3DResnet-VAE, DenseVoxelNetFix mrbrains,Brats2018,Brats2019, Iseg2019, IXI,MICCAI 2019 gleason challenge dataloadersAdd confusion matrix support for understanding training dynamicsSome Visualizations

Top priorities

Minimal test prediction example with pre-trained models Save produced 3d-total-segmentation as nifty files

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 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 ofmulti-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

版权声明：本文内容由网络用户投稿，版权归原作者所有，本站不拥有其著作权，亦不承担相应法律责任。如果您发现本站中有涉嫌抄袭或描述失实的内容，请联系我们jiasou666@gmail.com 处理，核实后本网站将在24小时内删除侵权内容。