Abstract: To address the problems in medical image segmentation,such as varying target sizes,diverse representations of the same anatomical structures across slices,and low distinction between organs and background leading to excessive redundant information,a novel model based on Gaussian bias self-attention and contextual cross attention,named Gaussian bias and cross attention U-Net(GCA-UNet),is proposed.The model utilizes residual modules to establish spatial prior hypotheses,employs Gaussian bias self-attention and external attention mechanisms to learn spatial priors and enhance feature representations of adjacent areas,and uses external attention to understand inter-sample correlations.The cross attention gated mechanism leverages multi-scale feature extraction to reinforce structural and boundary information while recalibrating contextual semantic information and filtering out redundant data.Experimental results on the Synapse abdominal CT multi-organ segmentation dataset and ACDC cardiac MRI dataset show that,the proposed GCA-UNet achieves Mean Dice accuracy metrics of 81.37% and 91.69%,respectively,with a Mean hd95 boundary precision of 16.01 on the Synapse dataset.Compared to other advanced medical image segmentation mo-dels,GCA-UNet offers higher segmentation accuracy with clearer tissue boundaries.

Key words: Medical image segmentation, U-shape network, Gaussian bias, External attention, Contextual cross attention gate