Abstract: A new colorectal polyp image segmentation method combining polarizing self-attention and Transformer is proposed to solve the problems of traditional colorectal polyp image segmentation such as insufficient target segmentation, insufficient contrast and blurred edge details. Firstly, an improved phase sensing hybrid module is designed to dynamically capture multi-scale context information of colorectal polyp images in Transformer to make target segmentation more accurate. Secondly, the polarization self-attention mechanism is introduced into the new method to realize the self-attention enhancement of the image, so that the obtained image features can be directly used in the polyp segmentation task to improve the contrast between the lesion area and the normal tissue area. In addition, the cue-cross fusion module is used to enhance the ability to capture the geometric structure of the image in dynamic segmentation, so as to improve the edge details of the resulting image. The experimental results show that the proposed method can not only effectively improve the precision and contrast of colorectal polyp segmentation, but also overcome the problem of blurred detail in the segmentation image. The test results on the data sets CVC-ClinicDB, Kvasir, CVC-ColonDB and ETIS-LaribPolypDB show that the proposed method can achieve better segmentation results, and the Dice similarity index is 0.946, 0.927, 0.805 and 0.781, respectively.