An image segmentation algorithm based on multi-scale feature pyramid network

Medical image segmentation is particularly critical as a prerequisite for relevant quantitative analysis in the treatment of clinical diseases. For example, in clinical cervical cancer radiotherapy, after acquiring subabdominal MRI images, a fast and accurate image segmentation of organs and tumors in MRI images can optimize the clinical radiotherapy process, whereas traditional approaches use manual annotation by specialist doctors, which is time consuming and laborious, therefore, automatic organ segmentation of subabdominal MRI images is a valuable research topic. In the field of automatic segmentation in medical image, U Net, proposed by Ronneberger et al. [1] in 2015, still has an irreplaceable influence today. Many transformers of U Net network are proposed, and various plug and play components use it as a backbone network [3 10]. Image semantic segmentation differs from image classification.