基于因果约束的Transformer医学图像分割方法OA

[Purpose]The data distribution has a significant impact on the performance of deep learning models.However,deep models may learn features irrelevant to the segmentation target,which is usually inapplicable for new datasets,resulting in insufficient generalization ability.[Methods]To alleviate this problem,this paper proposes a Transformer-based medical image segmentation method with causal restraint.Taking MCRformer as the main body of the net-work,the Morphological Constraint Stream module is used to extract morphological constraint prior information.The meshed Transformer further extracts local and network-level information.The method in-troduces a Causal Restraint module to alleviate the correlation between features related to regions of interest(ROI)and irrelative features.Representative features are selected for the model through morphological and caus-al prior information,ultimately improving segmentation performance.[Results]On the public Synapse dataset,the Dice Similarity Coefficient and Hausdorff Distance achieved mean values of 80.01%and 19.39mm,respec-tively.On the public ACDC dataset,the mean DSC reached 90.95%,outperforming other comparative methods.[Conclusions]Experiment results demonstrate that the proposed method effectively enhances multi-organ seg-mentation performance on CT and MRI images and validates the feasibility of the causal restraint module across different models.