MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images

Lei Li, Fuping Wu, Sihan Wang, Xinzhe Luo, Carlos Martín-Isla, Shuwei Zhai, Jianpeng Zhang, Yanfei Liu, Zhen Zhang, Markus J. Ankenbrand, Haochuan Jiang, Xiaoran Zhang, Linhong Wang, Tewodros Weldebirhan Arega, Elif Altunok, Zhou Zhao, Feiyan Li, Jun Ma, Xiaoping Yang, Elodie PuybareauIlkay Oksuz, Stephanie Bricq, Weisheng Li, Kumaradevan Punithakumar, Sotirios A. Tsaftaris, Laura M. Schreiber, Mingjing Yang, Guocai Liu, Yong Xia, Guotai Wang, Sergio Escalera, Xiahai Zhuang*

*Corresponding author for this work

Research output: Contribution to journalShort surveypeer-review

13 Citations (Scopus)

Abstract

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. Note that MyoPS refers to both myocardial pathology segmentation and the challenge in this paper. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore the potential of solutions, as well as to provide a benchmark for future research. The average Dice scores of submitted algorithms were 0.614±0.231 and 0.644±0.153 for myocardial scars and edema, respectively. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

Original languageEnglish
Article number102808
JournalMedical Image Analysis
Volume87
DOIs
Publication statusPublished - Jul 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Funding

This work was funded by the National Natural Science Foundation of China (grant no. 62111530195 and 61971142 ). All authors approved the final version of the manuscript.

FundersFunder number
National Natural Science Foundation of China62111530195, 61971142

    Keywords

    • Benchmark
    • Cardiac magnetic resonance
    • Multi-sequence MRI
    • Multi-source images
    • Myocardial pathology segmentation

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