SAMITorch 0.2.7

Deep Learning Framework For Medical Image Analysis

SAMITorch

Welcome to SAMITorch

SAMITorch is a deep learning framework for Shape Analysis in Medical Imaging laboratory of École de technologie supérieure using PyTorch library. It implements an extensive set of loaders, transformers, models and data sets suited for deep learning in medical imaging. Our objective is to build a tested, standard framework for quickly producing results in deep learning reasearch applied to medical imaging.

Table Of Contents

• Authors
• References
• Project architecture
  • Folder structure
  • Main Components
    • Models
    • Transformers
    • Configuration
    • Main
• Contributing
• Branch naming
• Commits syntax
• Acknowledgments

Authors

• Pierre-Luc Delisle - pldelisle
• Benoit Anctil-Robitaille - banctilrobitaille

References

Segmentation

@article{RN10,
   author = {Çiçek, Özgün and Abdulkadir, Ahmed and Lienkamp, Soeren S. and Brox, Thomas and Ronneberger, Olaf},
   title = {3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation},
   journal = {eprint arXiv:1606.06650},
   pages = {arXiv:1606.06650},
   url = {https://ui.adsabs.harvard.edu/\#abs/2016arXiv160606650C},
   year = {2016},
   type = {Journal Article}
}

Classification

@inproceedings{RN12,
   author = {He, K. and Zhang, X. and Ren, S. and Sun, J.},
   title = {Deep Residual Learning for Image Recognition},
   booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
   pages = {770-778},
   ISBN = {1063-6919},
   DOI = {10.1109/CVPR.2016.90},
   type = {Conference Proceedings}
}

Diffusion imaging

Application

Setup

pip install -r [path/to/requirements.txt]
python3 <main_script>.py

Project architecture

Folder structure

── samitorch
|    ├── configs                 - This folder contains the YAML configuration files.
|    │   ├── configurations.py       - This file contains the definitions of different configuration classes.
|    │   |── resnet3d.yaml           - Standard ResNet 3D configuration file and model definition.
|    │   └── unet3d.yaml             - Standard UNet 3D configuration file and model definition.
|    |
|    ├── initializers            - This folder contains custom layer/op initializers.  
|    |   └── initializers.py
|    │
|    ├── inputs                  - This folder contains anything relative to inputs to a network.
|    |   |── batch.py                - Contains Batch definition object used in training. 
|    |   |── datasets.py             - Contains basic dataset definition for classification and segmentation.
|    |   |── images.py               - Contains Enums for various methods.
|    |   |── patch.py                - Contains Patch definition used in segmentation problems.
|    |   |── sample.py               - Contains a Sample object.
|    |   |── transformers.py         - Contains a series of common transformations.
|    |   └── utils.py                - Contains various utilitary methods.
|    |   
|    ├── models                  - This folder contains any standard and tested deep learning models.
|    │   |── layers.py               - Contains layer definitions. 
|    |   |── resnet3d.py             - Contains a standard ResNet 3D model.
|    |   └── unet3d.py               - Contains a standard UNet 3D model.                   
|    |
|    |── parsers                 - This folder contains parsers definition used in SAMITorch.
|    |
|    ├── preprocessing           - This folder contains anything relative to input preprocessing, and scripts that must be executed prior training.
|    |
|    └── utils                   - This folder contains any utils you may need.
|         |── files.py              - Contains file related utils methods.
|         |── slice_builder.py      - Contains an object to build slices out of a data sets (for image segmentation).
|         └── tensors.py            - Contains tensor related utils methods.            
── tests                   - Folder containing unit tests of the standard framework api and functions.

Main components

(To be documented shortly...)

Models

Transformers

Configs

Main

Contributing

If you find a bug or have an idea for an improvement, please first have a look at our contribution guideline. Then,

• Create a branch by feature and/or bug fix
• Get the code
• Commit and push
• Create a pull request

Branch naming

Instance	Branch	Description, Instructions, Notes
Stable	stable	Accepts merges from Development and Hotfixes
Development	dev/ [Short description] [Issue number]	Accepts merges from Features / Issues and Hotfixes
Features/Issues	feature/ [Short feature description] [Issue number]	Always branch off HEAD or dev/
Hotfix	fix/ [Short feature description] [Issue number]	Always branch off Stable

Commits syntax

Adding code:

+ Added [Short Description] [Issue Number]

Deleting code:

- Deleted [Short Description] [Issue Number]

Modifying code:

* Changed [Short Description] [Issue Number]

Merging branches:

Y Merged [Short Description]

To build documentation

SAMITorch uses Sphinx Documentation. To build doc, simply execute the following:

cd docs
sphinx-build -b html source build

Acknowledgment

Thanks to École de technologie supérieure, Hervé Lombaert and Christian Desrosiers for providing us a lab and helping us in our research activities.

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