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https://github.com/ellisdg/3dunetcnn
Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation
https://github.com/ellisdg/3dunetcnn
Last synced: about 5 hours ago
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Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation
- Host: GitHub
- URL: https://github.com/ellisdg/3dunetcnn
- Owner: ellisdg
- License: mit
- Created: 2017-02-01T19:24:34.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2024-06-27T20:50:56.000Z (5 months ago)
- Last Synced: 2024-11-19T21:04:17.200Z (about 5 hours ago)
- Language: Python
- Homepage:
- Size: 19.1 MB
- Stars: 1,926
- Watchers: 57
- Forks: 651
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# 3D U-Net Convolution Neural Network
[[Update August 2023 - data loading is now 10x faster!](doc/Changes.md)]
* [Tutorials](#tutorials)
* [Introduction](#introduction)
* [Quick Start Guide](#quickstart)
* [Installation](#installation)
* [Example](#brats2020)
* [Documentation](#documentation)
* [Citation](#citation)
## Tutorials
### [Brain Tumor Segmentation (BraTS 2020)](examples/brats2020)
[](examples/brats2020)
## 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](https://github.com/ellisdg/3DUnetCNN/tree/master/examples/brats2020) for a description on how to create a configuration and train a model.
## Documentation
* [Configuration Guide](doc/Configuration.md)
* [Frequently Asked Questions](doc/FAQ.md)
### Still have questions?
Once you have reviewed the documentation, feel free to raise an issue on GitHub, or email me at [email protected].
## 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