A Deep Learning-Based Approach with Image-Driven Active Contour Loss for Medical Image Segmentation

Introduction

This repository contains the implementation for automated endocardium (Endo) and epicardium (Epi) segmentation on the ACDC-2017 dataset and Skin lesion segmentation on the ISIC-2018 dataset introduced in the following paper: "A Deep Learning-Based Approach with Image-Driven Active Contour Loss for Medical Image Segmentation" https://doi.org/10.1007/978-981-16-5120-5_1.

Our contributions

• Introducing a new loss function based on level set method.
• Building the end-to-end model for multiphase segmentation based on U-Net architecture

Results

Citation

If you find this reference implementation useful in your research, please consider citing:

@inproceedings{trinh2022deep,
  title={A Deep Learning-Based Approach with Image-Driven Active Contour Loss for Medical Image Segmentation},
  author={Trinh, Minh-Nhat and Nguyen, Nhu-Toan and Tran, Thi-Thao and Pham, Van-Truong},
  booktitle={Proceedings of International Conference on Data Science and Applications},
  pages={1--12},
  year={2022},
  organization={Springer}
}