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

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

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/).

Benchmark, Cardiac magnetic resonance, Multi-sequence MRI, Multi-source images, Myocardial pathology segmentation
1361-8415
Li, Lei
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Wu, Fuping
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Wang, Sihan
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Luo, Xinzhe
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Martín-Isla, Carlos
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Zhai, Shuwei
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Zhang, Jianpeng
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Liu, Yanfei
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Zhang, Zhen
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Ankenbrand, Markus J.
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Jiang, Haochuan
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Zhang, Xiaoran
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Wang, Linhong
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Arega, Tewodros Weldebirhan
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Altunok, Elif
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Zhao, Zhou
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Li, Feiyan
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Ma, Jun
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Yang, Xiaoping
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Puybareau, Elodie
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Oksuz, Ilkay
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Bricq, Stephanie
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Li, Weisheng
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Punithakumar, Kumaradevan
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Tsaftaris, Sotirios A.
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Schreiber, Laura M.
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Yang, Mingjing
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Liu, Guocai
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Xia, Yong
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Wang, Guotai
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Escalera, Sergio
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Zhuang, Xiahai
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Li, Lei
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Wu, Fuping
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Wang, Sihan
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Luo, Xinzhe
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Martín-Isla, Carlos
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Zhai, Shuwei
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Zhang, Jianpeng
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Liu, Yanfei
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Zhang, Zhen
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Ankenbrand, Markus J.
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Jiang, Haochuan
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Zhang, Xiaoran
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Wang, Linhong
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Arega, Tewodros Weldebirhan
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Altunok, Elif
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Zhao, Zhou
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Li, Feiyan
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Ma, Jun
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Yang, Xiaoping
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Puybareau, Elodie
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Oksuz, Ilkay
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Bricq, Stephanie
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Li, Weisheng
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Punithakumar, Kumaradevan
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Tsaftaris, Sotirios A.
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Schreiber, Laura M.
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Yang, Mingjing
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Liu, Guocai
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Xia, Yong
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Wang, Guotai
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Escalera, Sergio
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Zhuang, Xiahai
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Li, Lei, Wu, Fuping, Wang, Sihan, Luo, Xinzhe, Martín-Isla, Carlos, Zhai, Shuwei, Zhang, Jianpeng, Liu, Yanfei, Zhang, Zhen, Ankenbrand, Markus J., Jiang, Haochuan, Zhang, Xiaoran, Wang, Linhong, Arega, Tewodros Weldebirhan, Altunok, Elif, Zhao, Zhou, Li, Feiyan, Ma, Jun, Yang, Xiaoping, Puybareau, Elodie, Oksuz, Ilkay, Bricq, Stephanie, Li, Weisheng, Punithakumar, Kumaradevan, Tsaftaris, Sotirios A., Schreiber, Laura M., Yang, Mingjing, Liu, Guocai, Xia, Yong, Wang, Guotai, Escalera, Sergio and Zhuang, Xiahai (2023) MyoPS: a benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images. Medical Image Analysis, 87, [102808]. (doi:10.1016/j.media.2023.102808).

Record type: Review

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/).

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More information

Accepted/In Press date: 30 March 2023
e-pub ahead of print date: 4 April 2023
Published date: 21 April 2023
Keywords: Benchmark, Cardiac magnetic resonance, Multi-sequence MRI, Multi-source images, Myocardial pathology segmentation

Identifiers

Local EPrints ID: 488797
URI: http://eprints.soton.ac.uk/id/eprint/488797
ISSN: 1361-8415
PURE UUID: 66614e18-8d72-49ab-9625-1caf7bedd201
ORCID for Lei Li: ORCID iD orcid.org/0000-0003-1281-6472

Catalogue record

Date deposited: 05 Apr 2024 16:43
Last modified: 10 Apr 2024 02:14

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Contributors

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

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