Image Segmentation Using U-Net3+ Based on Manhattan Distance Self-attention Mechanism
In response to the problem that the current mainstream image segmentation algorithms have poor discrimination ability of pixels with similar features but different categories on the segmentation boundary,which affects segmentation accuracy,this paper designed a U-Net3+ segmentation algorithm based on the Manhattan distance self-attention mechanism.Large-scale contextual information relationships were modeled by focusing on the degree of difference in information representation between different feature points,thereby the network's ability was enhanced to distinguish pixels with similar features but different categories and learn global relationships.Then,different scale features are fused through the full-scale jump connection structure of U-Net3+,providing more scale contextual information for the network,making the segmentation network balance detailed information and global relationships,thereby improving the segmentation effect.Finally,this paper used the COVID-19 CT dataset to conduct experimental tests on the algorithm.The results showed that after the introduction of the Manhattan-distance-based self-attention mechanism,the Dice and IoU metrics of U-Net3+ were improved by 2.79%and 3.17%respectively,compared with the U-Net3+ using the multiple self-attention mechanism improved by 1.06%and 1.02%,Which proves the algorithm to be of certain effectiveness and superiority.
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