Abstract: To address the challenges of organ edge blurring and significant differences in organ proportions in abdominal CT multi-organ segmentation tasks,this paper proposes a complex organ segmentation approach based on edge constraints and enhanced Swin Unetr.To extract features of varying degrees of granularity from organs with different voxel proportions,this study introduces the Masked Attention Block.By computing the mask information of each organ,the corresponding features are extracted.Subsequently,based on dataset priors and mask information,refined feature extraction is conducted within appropriate window and block sizes to obtain the fine-grained features necessary for segmenting organs with smaller voxel proportions.Upon generating preliminary semantic segmentation predictions,to fully leverage boundary information and enhance the model’s ability to handle such information,the semantic features are further extracted through convolutional layers to capture boundary details.The model’s semantic segmentation results are constrained by the edge prediction task through edge loss minimization.The proposed method is trained and tested on the BTCV and TCIA pancreas-CT datasets.The enhancement modules are incorporated into the UNet+＋based on convolutional networks and the Swin Unetr based on Transformers for training.Comparative experiments are conducted with classic networks such as Unetr.On the BTCV dataset,the Dice coefficients reach 0.847 9 and 0.840 6,with corresponding Hausdorff distances of 11.76 and 8.35,respectively.Overall,the proposed method outperforms other compa-rative methods,confirming its effectiveness and feasibility.

Key words: Image segmentation, Attention mechanism, Computed tomography segmentation, Deep learning, Multi-organ segmentation, Multi-task learning, Computer-aided diagnosis