Cascaded Segmentation Network based on Double Branch Boundary Enhancement

Cervical cancer is one of the most common causes of cancer death in women. During the treatment of cervical cancer, it is necessary to make a radiation plan based on the clinical target volume (CTV) on the CT image. At present, CTV is manually sketched by physicists, which is time-consuming and laborious. With the help of deep learning model, computer can accurately draw the outline of CTV in Colleges and universities. The CDBNet proposed in this paper is a cascaded segmentation network based on double-branch boundary enhancement. First, classification network determines whether a single image contains a region of interest (ROI), and then the segmentation network uses DBNet to segment more accurately at the ROI contour. In this paper, we propose CDBNet, a cascaded segmentation network based on doublebranch boundary enhancement. First, classification network determines whether a single image contains a region of interest (ROI), and then the segmentation network uses DBNet to segment more accurately at the ROI contour. The CDBNet proposed in this paper was verified on the cervical cancer dataset provided by the Department of Radiation Oncology, West China Hospital, Sichuan Province. The average dice and 95HD of the delineation results are 86.12% and 2.51mm. At the same time, the classification accuracy rate of whether the image contains ROI can reach 93.19%, and the average Dice of the image containing ROI can reach 70%.