Automatic　segmentation　of　left　atrial　(LA)　scars　from　late　gadolinium　enhanced　CMR　images　is　a　crucial　step　for　atrial　fibrillation　(AF)　recurrence　analysis.　However,　delineating　LA　scars　is　tedious　and　error-prone　due　to　the　variation　of　scar　shapes.　In　this　work,　we　propose　a　boundary-aware　LA　scar　segmentation　network,　which　is　composed　of　two　branches　to　segment　LA　and　LA　scars,　respectively.　We　explore　the　inherent　spatial　relationship　between　LA　and　LA　scars.　By　introducing　a　Sobel　fusion　module　between　the　two　segmentation　branches,　the　spatial　information　of　LA　boundaries　can　be　propagated　from　the　LA　branch　to　the　scar　branch.　Thus,　LA　scar　segmentation　can　be　performed　condition　on　the　LA　boundaries　regions.　In　our　experiments,　40　labeled　images　were　used　to　train　the　proposed　network,　and　the　remaining　20　labeled　images　were　used　for　evaluation.　The　network　achieved　an　average　Dice　score　of　0.608　for　LA　scar　segmentation.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.