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arXiv 2201.03186eess.IVcs.CV

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Fudan University(复旦大学);Shanghai Jiao Tong University(上海交通大学);Universitat de Barcelona(巴塞罗那大学);Universitat Autònoma de Barcelona(巴塞罗那自治大学);University of Electronic Science and Technology of China(电子科技大学);Northwestern Polytechnical University(西北工业大学);Hunan University(湖南大学);National Engineering Laboratory for Robot Visual Perception and Control Technology(机器人视觉感知与控制技术国家工程实验室);Fuzhou University(福州大学);Comprehensive Heart Failure Center(综合心力衰竭中心)
Lei Li, Fuping Wu, Sihan Wang, Xinzhe Luo, Carlos Martin-Isla, Shuwei Zhai, Jianpeng Zhang, Yanfei Liu7, Zhen Zhang, Markus J. Ankenbrand, Haochuan Jiang, Xiaor… 展开作者

Lei Li, Fuping Wu, Sihan Wang, Xinzhe Luo, Carlos Martin-Isla, Shuwei Zhai, Jianpeng Zhang, Yanfei Liu7, 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 Puybareau, Ilkay 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

英文摘要:

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on 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. 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 potential of solutions, as well as to provide a benchmark for future research. 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. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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