  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/).
