Myocardial　pathology　segmentation　(MyoPS)　is　critical　for　the　risk　stratification　and　treatment　planning　of　myocardial　infarction　(MI).　Multi-sequence　cardiac　magnetic　resonance　(MS-CMR)　images　can　provide　valuable　information.　For　instance,　balanced　steady-state　free　precession　cine　sequences　present　clear　anatomical　boundaries,　while　late　gadolinium　enhancement　and　T2-weighted　CMR　sequences　visualize　myocardial　scar　and　edema　of　MI,　respectively.　Existing　methods　usually　fuse　anatomical　and　pathological　information　from　different　CMR　sequences　for　MyoPS,　but　assume　that　these　images　have　been　spatially　aligned.　However,　MS-CMR　images　are　usually　unaligned　due　to　the　respiratory　motions　in　clinical　practices,　which　poses　additional　challenges　for　MyoPS.　This　work　presents　an　automatic　MyoPS　framework　for　unaligned　MS-CMR　images.　Specifically,　we　design　a　combined　computing　model　for　simultaneous　image　registration　and　information　fusion,　which　aggregates　multi-sequence　features　into　a　common　space　to　extract　anatomical　structures　(i.e.,　myocardium).　Consequently,　we　can　highlight　the　informative　regions　in　the　common　space　via　the　extracted　myocardium　to　improve　MyoPS　performance,　considering　the　spatial　relationship　between　myocardial　pathologies　and　myocardium.　Experiments　on　a　private　MS-CMR　dataset　and　a　public　dataset　from　the　MYOPS2020　challenge　show　that　our　framework　could　achieve　promising　performance　for　fully　automatic　MyoPS.　IEEE