Abstract: Medical microscopic image segmentation has important application value in clinical diagnosis and pathological analysis.However,due to the complex visual features such as shape,texture,and size of microscopic images,accurate segmentation of these images is a challenging task.In this paper,we propose a new segmentation model called UMSTC,which is based on a U-shaped structure and combines the U-Net model and Swin Transformer model to balance the details and macro features of images while maintaining modeling integrity.Specifically,the down-sampling part of the UMSTC model uses the Swin Transformer network to optimize its inherent attention mechanism for extracting micro and macro features,while the up-sampling part is based on a CNN network's deconvolution operation and uses a residual mechanism to receive and fuse feature maps from the down-sampling stage to reduce image synthesis accuracy loss.Experimental results show that the proposed UMSTC segmentation model has better segmentation performance than current mainstream medical image semantic segmentation models,with mPA and mIoU increases by approximately 3%~ 5% and 3%~8%,respectively,and the segmentation results have higher subjective visual quality and fewer artifacts.Therefore,the UMSTC model has broad application prospects in the field of medical microscopic image segmentation.

Key words: Microscopic image segmentation, Swin Transformer, CNN, Attention mechanism, Residual network