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/).