Atrial　fibrillation　(AF)　is　a　prevalent　heart　rate　arrhythmia　and　its　incidence　is　increasing　with　the　aging　population.　The　late　gadoliniumenhanced　magnetic　resonance　imaging　(LGE-MRI)　provides　pathologic　changes　in　the　left　atrium,　allowing　for　a　detailed　assessment　of　the　left　atrial　anatomy,　which　is　critical　for　diagnosis　and　treatment　decisions　in　AF.　The　segmentation　performance　of　current　left　atrial　segmentation　methods　is　significantly　degraded　when　applied　to　multicenter　data.　In　this　work,　we　propose　ResCAUNet,　a　deep　learning　method　based　on　residual　neural　networks.　Specifically,　a　pre-trained　model　driven　residual　segmentation　network　is　first　designed　to　alleviate　the　problem　of　gradient　disappearance　and　help　to　extract　more　complex　image　features.　Secondly,　an　adaptive　scale　weight　loss　function　was　introduced　to　solve　the　problem　of　discontinuous　segmentation　boundary,　so　as　to　ensure　higher　accuracy　of　object　segmentation.　Furthermore,　the　coordinate　attention(CA)　mechanism　is　introduced　for　adaptive　weight　allocation,　focusing　on　the　key　parts　of　the　image　to　improve　the　accuracy　of　left　atrial　reconstruction.　Our　method　is　evaluated　on　the　LAScarQS2024　validation　set　and　achieves　an　average　Dice　of　0.853.　The　evaluation　results　show　that　the　proposed　method　is　effective　in　left　atrium　segmentation　of　LGE-MRI.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2025.