By Mahdi Biparva (PhD in Computer Science)

This package implements deep learning modules for medical imaging application in PyTorch. It contains different modules in the data-pipeline such as the data-loaders, data-containers, transformations etc. In the model-pipeline, there are several segmentation neural networks, training logics, loss function, metrics etc.

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If you find "miTorch: Medical Imaging in PyTorch" useful in your research, please consider citing the research paper:

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1. Introduction
2. Features
3. Requirements: Software
4. Requirements: Hardware
5. Installation
6. Prerequisites
7. Preparation
8. Demo: 3D Medical Segmentation
9. Demo: Self-Supervised Pre-Training
10. Future Work
11. Contributors

The primary goal is to have a solid, readable, modular, reliable, extendable PyTorch package for medical imaging application in deep learning. The input is 3D volumes of various modalities and the task could be segmentation, classification, and transfer learning.

To name a few, the learning tasks are:

• 3D Medical Segmentation:
  • Head-From-Brain / Skull-Stripping (HFB)
  • White-Matter-Hyperintensities (WMH)
• Robustness analysis test pipeline
• Self-supervised learning

miTorch has currently the following capabilities and components:

• Robust 3D data loading modules for:
  • CT/MRI datasets:
    • Skull-stripping
    • White-matter hyperintensities
  • Electron-Microscopy datasets:
    • Neuron segmentation (counting)
    • Axon/Virus Tracing (Tractography)
    • Hippocampal Subfield Segmentation (multi-label)
• 3D data transformations and pipeline generation:
  • Spatial:
    • Cropping
    • Resizing
    • Axis Rotations
    • Flipping
    • Affine Transformations (Translation, Rotation, Scale, Shear)
  • Intensity:
    • Additive noise (Gaussian, Rice, etc)
    • Corrections (Gamma, Brightness, Contrast)
    • Bias Field
    • Blur
• Automatic Data transformation randomization
• Modular data-pipeline prototyping on-the-fly
• Seamless online patching and batching mechanisms
• Automatic Test Pipeline Generation and Evaluation
• Model Zoo containing:
  • Unet3D
  • Unet3D++ (NestedUnet3D)
  • CBAM
  • DUNet
  • DenseNet
  • SENet
  • VNet
  • DYNUnet
  • HighresNet
• Seamless 3D to 2D network conversion capability
• Various losses such as:
  • Dice
  • Focal
  • Hausdorff
  • Lovasz
  • MSE
• Weighted multi-loss training
• Various metrics such as
  • Jaccard
  • Hausdorff
  • Dice
  • F1
  • Relative Volume Difference
• Hyper-parameter optimization modes:
  • Manual grid search
  • Bayesian semi-automatic optimization search (GPyTorch|BoTorch|Ax)
  • Visualization and Logging (Tensorboard)
• Test Evaluation:
  • Automatic test transformation pipeline generation
  • Batch evaluation and result logging for analysis
• Checkpointing models
• Logging and Visualization (using Tensorboard)
• Automatic-Mixed-Precision (AMP) feature
• Data-Distributed feature (supporting AMP):
• Data Parallel (with GIL)
• Distributed Data Parallel (no GIL, multi node multi GPU)
• Model-Parallel (Under development)

Currently it relies on Python and PyTorch ecosystem.

GPU devices with CUDA capabilities are required.

There is no installation needed at this moment. You would simply need to call the main function.

• Python 3.7
• PyTorch 1.4.0 (not tested on higher versions)
• CUDA 10.0 or higher

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We are aiming to develop self-supervised learning modules to enhance the segmentation robustness.

• Mahdi Biparva (core modeling and development)
• Parsa Esfahanian (self-supervised development)
• Braedyn Au (Tracing Segmentation development)
• Parisa Mojiri (experimentation)
• Lyndon Boone (experimentation)
• Maged Goubran (abstraction and methodologies)