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

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}
}