/3DUnetCNN

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

Primary LanguagePythonMIT LicenseMIT

3D U-Net Convolution Neural Network

Brain Tumor Segmentation (BraTS) Tutorial

Tumor Segmentation Example

Automatic Cranial Implant Design (AutoImpant)

Segmentation Example

Anatomical Barriers to Cancer Spread (ABCS)

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.

Dependencies

  • Pytorch
  • nilearn
  • pandas
  • keras

Pretrained Models

Got Questions?

Join the Slack group or email me at davidgellis2@gmail.com.

Citation

Coming soon.

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