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Dec 15, 2019
Sep 22, 2020

3D Medical Image Segmentation With Distance Transform Maps

Motivation: How Distance Transform Maps Boost Segmentation CNNs (MIDL 2020)

Incorporating the distance Transform maps of image segmentation labels into CNNs-based segmentation tasks has received significant attention in 2019. These methods can be classified into two main classes in terms of the main usage of distance transform maps.

  • Designing new loss functions
  • Adding an auxiliary task, e.g. distance map regression

Overview

However, with these new methods on the one hand and the diversity of the specific implementations and dataset-related challenges on the other, it's hard to figure out which design can generalize well beyond the experiments in the original papers. In this repository, we want to re-implement these methods (published in 2019) and evaluate them on the same 3D segmentation tasks (heart and liver tumor segmentation).

Experiments

Task LA Contributor GPU LiTS Contributor GPU
Boundary loss Yiwen Zhang 2080ti Mengzhang Li TITIAN RTX
Hausdorff loss Yiwen Zhang 2080ti Mengzhang Li TITIAN RTX
Signed distance map loss (AAAI 2020) Zhan Wei 1080ti cancel -
Multi-Head: FG DTM regression-L1 Yiwen Zhang 2080ti cancel -
Multi-Head: FG DTM regression-L2 Jianan Liu 2080ti cancel -
Multi-Head: FG DTM regression-L1 + L2 Gaoxiang Chen 2080ti cancel -
Multi-Head: SDF regression-L1 Feng Cheng TITAN X Chao Peng TITAN RTX
Multi-Head: SDF regression-L2 Rongfei Lv TITAN RTX Rongfei Lv TITAN RTX
Multi-Head: SDF regression-L1+L2 Yixin Wang P100 cancel -
Add-Branch: FG DTM regression-L1 Yaliang Zhao TITAN RTX cancel -
Add-Branch: FG DTM regression-L2 Mengzhang Li TITIAN RTX cancel -
Add-Branch: FG DTM regression-L1+L2 Yixin Wang P100 cancel -
Add-Branch: SDF regression-L1 Feng Cheng TITAN X Yixin Wang TITAN RTX
Add-Branch: SDF regression-L2 Feng Cheng TITAN X Yixin Wang P100
Add-Branch: SDF regression-L1+L2 Yixin Wang P100 Yunpeng Wang TITAN XP

Here is the code, and trained modles can be downloaded from Baidu Disk (pw:mgn0).

Related Work in 2019

New loss functions

Date First author Title Official Code Publication
2019 Yuan Xue Shape-Aware Organ Segmentation by Predicting Signed Distance Maps None AAAI 2020
2019 Hoel Kervadec Boundary loss for highly unbalanced segmentation pytorch MIDL 2019
2019 Davood Karimi Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks (arxiv) None TMI 2019

Auxiliary tasks

Date First author Title Official Code Publication
2019 Yan Wang Deep Distance Transform for Tubular Structure Segmentation in CT Scans None CVPR2020
2019 Shusil Dangi A Distance Map Regularized CNN for Cardiac Cine MR Image Segmentation (arxiv) None Medical Physics
2019 Fernando Navarro Shape-Aware Complementary-Task Learning for Multi-organ Segmentation (arxiv) None MICCAI MLMI 2019
2019 Balamurali Murugesan Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation (arXiv) None EMBC
2019 Balamurali Murugesan Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation (arXiv) Pytorch MLMI

Acknowledgments

The authors would like to thank the organization team of MICCAI 2017 liver tumor segmentation challenge MICCAI 2018 and left atrial segmentation challenge for the publicly available dataset. We also thank the reviewers for their valuable comments and suggestions. We appreciate Cheng Chen, Feng Cheng, Mengzhang Li, Chengwei Su, Chengfeng Zhou and Yaliang Zhao to help us finish some experiments. Last but not least, we thank Lequan Yu for his great PyTorch implementation of V-Net and Fabian Isensee for his great PyTorch implementation of nnU-Net.

Including the following citation in your work would be highly appreciated.

@inproceedings{ma-MIDL2020-SegWithDist,
  title={How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study},
  author={Ma, Jun and Wei, Zhan and Zhang, Yiwen and Wang, Yixin and Lv, Rongfei and Zhu, Cheng and Chen, Gaoxiang and Liu, Jianan and Peng, Chao and Wang, Lei and Wang, Yunpeng and Chen, Jianan},
  booktitle={Medical Imaging with Deep Learning},
  pages = {479--492},
  volume = {121},
  month = {06--08 Jul},
  year={2020},
  series = {Proceedings of Machine Learning Research},
  editor = {Tal Arbel and Ismail Ben Ayed and Marleen de Bruijne and Maxime Descoteaux and Herve Lombaert and Christopher Pal},
  publisher = {PMLR},
  url = {http://proceedings.mlr.press/v121/ma20b.html}
}

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