The complementation of arterial and venous phases visual information of CTs can help better distinguish the pancreas from its surrounding structures. However, the exploration of cross-phase contextual information is still under research in computer-aided pancreas segmentation. This paper presents M${}^3$Net, a framework that integrates multi-scale multi-view information for multi-phase pancreas segmentation. The core of M${}^3$Net is built upon a dual-path network in which individual branches are set up for two phases. Cross-phase interactive connections bridging the two branches are introduced to interleave and integrate dual-phase complementary visual information. Besides, we further devise two types of non-local attention modules to enhance the high-level feature representation across phases. First, we design a location attention module to generate cross-phase reliable feature correlations to suppress the misalignment regions. Second, the depth-wise attention module is used to capture the channel dependencies and then strengthen feature representations. The experiment data consists of 224 internal CTs (106 normal and 118 abnormal) with 1 mm slice thickness, and 66 external CTs (29 normal and 37 abnormal) with 5 mm slice thickness. We achieve new state-of-the-art performance with average DSC of 91.19% on internal data, and promising result with average DSC of 86.34% on external data.

CT 动脉期和静脉期视觉信息的互补有助于更好地区分胰腺与其周围结构。然而，跨阶段上下文信息的探索仍在计算机辅助胰腺分割的研究中。本文介绍了 M${}^3$Net，一个集成多尺度多视图信息的框架，用于多相胰腺分割。M的核心${}^3$Net 建立在双路径网络之上，其中为两个阶段设置了单独的分支。引入桥接两个分支的跨阶段交互连接，以交织和集成双阶段互补视觉信息。此外，我们进一步设计了两种类型的非局部注意力模块来增强跨阶段的高级特征表示。首先，我们设计了一个位置注意模块来生成跨阶段可靠的特征相关性，以抑制错位区域。其次，深度注意模块用于捕获通道依赖关系，然后加强特征表示。实验数据包括 224 个内部 CT（106 个正常和 118 个异常），层厚为 1 mm，66 个外部 CT（29 个正常，37 个异常），层厚为 5 mm。