Sutassananon K.Kusakunniran W.Orgun M.Siriapisith T.Mahidol University2025-12-022025-12-022025-12-01Scientific Reports Vol.15 No.1 (2025)https://repository.li.mahidol.ac.th/handle/123456789/113355Accurate segmentation in medical imaging requires loss functions that capture both regional overlap and boundary alignment. This study evaluates composite losses combining binary cross-entropy (BCE) and a boundary-based term under fixed and adaptive weighting schemes, using U-Net and SwinUNETR on the MM-WHS dataset. For U-Net, a small boundary contribution with adaptive weighting yielded the best results: Standard SoftAdapt (90/10 BCE + BoundaryDoU) achieved the highest Dice score (), surpassing both the baseline () and fixed ratios. In contrast, SwinUNETR achieved its strongest performance with a fixed 70% BCE + 10% boundary ratio (0.919 ± 0.02). The result showed that combining a boundary-based loss term helps improve the segmentation accuracy. However, the performance gain is dependent on the architecture of the segmentation model; convolution-based U-Net benefited from the adaptive loss weighting scheme, whereas Transformer-based SwinUNETR without strong inductive bias did not benefit from increased influence of the boundary loss term.MultidisciplinaryArticleSCOPUS10.1038/s41598-025-25785-92-s2.0-10502285126120452322