Myocardial　infarction　(MI)　is　a　leading　cause　of　death　worldwide.　Late　gadolinium　enhancement　(LGE)　and　T2-weighted　cardiac　magnetic　resonance　(CMR)　imaging　can　respectively　identify　scarring　and　edema　areas,　both　of　which　are　essential　for　MI　risk　stratification　and　prognosis　assessment.　Although　combining　complementary　information　from　multi-sequence　CMR　is　useful,　acquiring　these　sequences　can　be　time-consuming　and　prohibitive,　e.g.,　due　to　the　administration　of　contrast　agents.　Cine　CMR　is　a　rapid　and　contrast-free　imaging　technique　that　can　visualize　both　motion　and　structural　abnormalities　of　the　myocardium　induced　by　acute　MI.　Therefore,　we　present　a　new　end-to-end　deep　neural　network,　referred　to　as　CineMyoPS,　to　segment　myocardial　pathologies,　i.e.,　scars　and　edema,　solely　from　cine　CMR　images.　Specifically,　CineMyoPS　extracts　both　motion　and　anatomy　features　associated　with　MI.　Given　the　interdependence　between　these　features,　we　design　a　consistency　loss　(resembling　the　co-training　strategy)　to　facilitate　their　joint　learning.　Furthermore,　we　propose　a　time-series　aggregation　strategy　to　integrate　MI-related　features　across　the　cardiac　cycle,　thereby　enhancing　segmentation　accuracy　for　myocardial　pathologies.　Experimental　results　on　a　multi-center　dataset　demonstrate　that　CineMyoPS　achieves　promising　performance　in　myocardial　pathology　segmentation,　motion　estimation,　and　anatomy　segmentation.　©　1982-2012　IEEE.