/3DUnetCNN

Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

Primary LanguagePythonMIT LicenseMIT

3D U-Net Convolution Neural Network

[Update August 2023 - data loading is now 10x faster!]

Tutorials

Brain Tumor Segmentation (BraTS 2020)

Tumor Segmentation Example

Introduction

We designed 3DUnetCNN to make it easy to apply and control the training and application of various deep learning models to medical imaging data. The links above give examples/tutorials for how to use this project with data from various MICCAI challenges.

Quick Start Guide

How to train a UNet on your own data.

Installation

  1. Clone the repository:
    git clone https://github.com/ellisdg/3DUnetCNN.git

  2. Install the required dependencies*:
    pip install -r 3DUnetCNN/requirements.txt

*It is highly recommended that an Anaconda environment or a virtual environment is used to manage dependcies and avoid conflicts with existing packages.

Create configuration file and run training

See the Brats 2020 example for a description on how to create a configuration and train a model.

Documentation

Still have questions?

Once you have reviewed the documentation, feel free to raise an issue on GitHub, or email me at david.ellis@unmc.edu.

Citation

Ellis D.G., Aizenberg M.R. (2021) Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework. In: Crimi A., Bakas S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science, vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_4

Additional Citations

Ellis D.G., Aizenberg M.R. (2020) Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge. In: Li J., Egger J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science, vol 12439. Springer, Cham. https://doi.org/10.1007/978-3-030-64327-0_6

Ellis, D.G. and M.R. Aizenberg, Structural brain imaging predicts individual-level task activation maps using deep learning. bioRxiv, 2020: https://doi.org/10.1101/2020.10.05.306951