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...
- 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. For more background in Deep Learning in MRI advice this.
- Alternatively, you can create a virtual environment and install the requirements. Check the installation folder for more instructions.
- You can also take a quick glance at the manual.
- If you do not have a capable environment or device to run this projects then you could give Google Colab a try. It allows you to run the project using a GPU device, free of charge. You may try our Colab demo using this notebook:
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U-Net3D Learning Dense Volumetric Segmentation from Sparse Annotation Learning Dense Volumetric Segmentation from Sparse Annotation
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V-net Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
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ResNet3D-VAE 3D MRI brain tumor segmentation using auto-encoder regularization
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U-Net Convolutional Networks for Biomedical Image Segmentation
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SkipDesneNet3D 3D Densely Convolutional Networks for Volumetric Segmentation
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HyperDense-Net A hyper-densely connected CNN for multi-modal image segmentation
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multi-stream Densenet3D A hyper-densely connected CNN for multi-modal image segmentation
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DenseVoxelNet Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
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MED3D Transfer learning for 3D medical image analysis
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HighResNet3D On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
| Task | Data Info/ Modalities | Train/Test | Volume size | Classes | Dataset size (GB) |
|---|---|---|---|---|---|
| Iseg 2017 | T1, T2 | 10 / 10 | 144x192x256 | 4 | 0.72 |
| Iseg 2019 | T1, T2 | 10 / 13 | 144x192x256 | 4 | 0.75 |
| MICCAI BraTs2018 | FLAIR, T1w, T1gd,T2w | 285 / - | 240x240x155 | 9 or 4 | 2.4 |
| MICCAI BraTs2019 | FLAIR, T1w, T1gd,T2w | 335 / 125 | 240x240x155 | 9 or 4 | 4 |
| Mrbrains 2018 | FLAIR, T1w, T1gd,T2w | 8 | 240x240x48 | 9 or 4 | 0.5 |
| IXI brain development Dataset | T1,T2 no labels | 581 | (110~150)x256x256 | - | 8.7 |
| MICCAI Gleason 2019 Challenge | 2D pathology images | ~250 | 5K x 5K | - | 2.5 |
| Model | # Params (M) | MACS(G) | Iseg 2017 DSC (%) | Mr-brains 4 classes DSC (%) |
|---|---|---|---|---|
| Unet3D | 17 M | 0.9 | 93.84 | 88.61 |
| Vnet | 45 M | 12 | 87.21 | 84.09 |
| DenseNet3D | 3 M | 5.1 | 81.65 | 79.85 |
| SkipDenseNet3D | 1.5 M | 31 | - | - |
| DenseVoxelNet | 1.8 M | 8 | - | - |
| HyperDenseNet | 10.4 M | 5.8 | - | - |
- 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.
We provide some implementations around Covid-19 for humanitarian purposes. In detail:
- COVID-Net A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images
- 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,Brats2019, Iseg2019, IXI,MICCAI 2019 gleason challenge dataloaders
- Add confusion matrix support for understanding training dynamics
- Some Visualizations
- Minimal test prediction example with pre-trained models
- Save produced 3d-total-segmentation as nifty files
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 :) !
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.
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}}
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:
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HyperDenseNet model
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Most of the segmentation losses from here
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3D-SkipDenseNet model from here
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3D-ResNet base model from here
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Abstract model class from MimiCry project
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Trainer and Writer class from PyTorch template
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Covid-19 implementation based on our previous work from here
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MICCAI 2019 Gleason challenge data-loaders based on our previous work from here
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Basic 2D Unet implementation from here
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Vnet model from here