Distance Matters: A Distance-Aware Medical Image Segmentation Algorithm

Abstract

The transformer-based U-Net network structure has gained popularity in the field of medical image segmentation. However, most networks overlook the impact of the distance between each patch on the encoding process. This paper proposes a novel GC-TransUnet for medical image segmentation. The key innovation is that it takes into account the relationships between patch blocks based on their distances, optimizing the encoding process in traditional transformer networks. This optimization results in improved encoding efficiency and reduced computational costs. Moreover, the proposed GC-TransUnet is combined with U-Net to accomplish the segmentation task. In the encoder part, the traditional vision transformer is replaced by the global context vision transformer (GC-VIT), eliminating the need for the CNN network while retaining skip connections for subsequent decoders. Experimental results demonstrate that the proposed algorithm achieves superior segmentation results compared to other algorithms when applied to medical images.