Multi-Stage Pancreas Localization and Segmentation Combined with Slices Context Information

Current deep learning-based pancreas segmentation mainly has the following problems: The anatomical specificity of the pancreas makes the deep network model easily disturbed by complex background; In the traditional two-stage segmentation method, the input of the coarse segmentation is the entire CT image, which leads to inaccurate localization based on the segmentation results; The traditional two-stage segmentation ignores the context information between adjacent slices, which limits the localization and subsequent segmentation results.In order to solve the problems above, a multi-stage pancreas localization and segmentation method combined with slices context information is proposed.In the first stage, anatomical prior locating is used to roughly shrink the input area; in the second stage, the proposed DASU-Net is used for coarse segmentation, and then the segmentation results are optimized with slices context information; last stage, single slice locating is used to further shrink irrelevant background, and then fine segmentation is completed by DASU-Net.The experimental results show that the proposed method can effectively improve the accuracy of pancreas segmentation. © 2021, Chinese Institute of Electronics. All right reserved.