# 3DUnetCNN

**Repository Path**: dr_yingli/Unet3dcnn

## Basic Information

- **Project Name**: 3DUnetCNN
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-03-25
- **Last Updated**: 2022-03-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 3D U-Net Convolution Neural Network
## [Brain Tumor Segmentation (BraTS) Tutorial](examples/brats2020)
[![Tumor Segmentation Example](legacy/doc/tumor_segmentation_illusatration.gif)](examples/brats2020)
## [Automatic Cranial Implant Design (AutoImpant)](examples/autoimplant2020)
[![ Segmentation Example](doc/AutoImplant-Viz.png)](examples/autoimplant2020)
## [Anatomical Barriers to Cancer Spread (ABCS)](examples/abcs2020)
## Background
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.

## Getting started
Install [PyTorch](https://pytorch.org/get-started/locally/) and 
[nilearn](https://nilearn.github.io/introduction.html#installing-nilearn).

## [Pretrained Models](https://zenodo.org/record/4289225)

## Got Questions?
See [FAQ](doc/FAQ.md), 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