SegWithDistMap is a comprehensive project that explores various methods of incorporating distance transform maps into CNN-based medical image segmentation tasks. This repository implements and evaluates multiple approaches published in 2019, focusing on 3D segmentation tasks for heart and liver tumor segmentation.
Key features:
- Implementation of various distance transform map methods:
- New loss functions (e.g., Boundary loss, Hausdorff loss)
- Auxiliary tasks (e.g., distance map regression)
- Evaluation on 3D medical image segmentation tasks
- Comparison of different approaches on the same datasets
- Extensive experiments with various GPU setups
This project provides valuable insights into the effectiveness of distance transform maps in improving segmentation performance across different medical imaging tasks.