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.