This is an official repository for the paper of "Mumford–Shah Loss Functional for Image Segmentation With Deep Learning" published in IEEE Transactions on Image Processing (TIP).

Paper

• Mumford–Shah Loss Functional for Image Segmentation With Deep Learning
  • Authors: Boah Kim and Jong Chul Ye

Implementation

A PyTorch implementation of deep-learning-based segmentation based on original cycleGAN code. (*Thanks for Jun-Yan Zhu and Taesung Park, and Tongzhou Wang.)

• Requirements
  • OS : Ubuntu
  • Python 2.7
  • PyTorch 1.1.0

Main

• Training: LiTS_train_unet.py which is handled by scripts/LiTS_train_unet.sh
• A code for Mumford-Shah loss functional is in models/loss.py.
  • 'levelsetLoss' and 'gradientLoss2d' classes compose our Mumford-Shah loss function.

Citation

@article{kim2019mumford,
  title={Mumford--Shah loss functional for image segmentation with deep learning},
  author={Kim, Boah and Ye, Jong Chul},
  journal={IEEE Transactions on Image Processing},
  volume={29},
  pages={1856--1866},
  year={2019},
  publisher={IEEE}
}