【摘要】 当前基于深度学习的胰腺分割主要存在以下问题:(1)胰腺的解剖特异性导致深度网络模型容易受到复杂多变背景的干扰;(2)传统两阶段分割方法在粗分割阶段将整张CT图像作为输入,导致依赖粗分割结果得到的定位不够准确;(3)传统两阶段分割方法忽略了切片间的上下文信息,限制了定位和后续分割结果的提升.针对上述问题,本文提出了结合切片上下文信息的多阶段胰腺定位与分割方法.第一阶段利用解剖先验定位粗略缩小输入区域;第二阶段先使用所设计的DASU-Net进行粗略分割,接着利用切片上下文信息优化分割结果;第三阶段使用单张切片定位进一步减少不相关背景,并使用DASU-Net完成精细分割.实验结果表明,本文所提方法能够有效提高胰腺分割的准确率.更多还原

【Abstract】 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.更多还原