Automatic segmentation of abdominal organs in computed tomography (CT) images can support radiation therapy and image-guided surgery workflows. Developing of such automatic solutions remains challenging mainly owing to complex organ interactions and blurry boundaries in CT images. To address these issues, we focus on effective spatial context modeling and explicit edge segmentation priors. Accordingly, we propose a 3D network with four main components trained end-to-end including shared encoder, edge detector, decoder with edge skip-connections (ESCs) and recurrent feature propagation head (RFP-Head). To capture wide-range spatial dependencies, the RFP-Head propagates and harvests local features through directed acyclic graphs (DAGs) formulated with recurrent connections in an efficient slice-wise manner, with regard to spatial arrangement of image units. To leverage edge information, the edge detector learns edge prior knowledge specifically tuned for semantic segmentation by exploiting intermediate features from the encoder with the edge supervision. The ESCs then aggregate the edge knowledge with multi-level decoder features to learn a hierarchy of discriminative features explicitly modeling complementarity between organs' interiors and edges for segmentation. We conduct extensive experiments on two challenging abdominal CT datasets with eight annotated organs. Experimental results show that the proposed network outperforms several state-of-the-art models, especially for the segmentation of small and complicated structures (gallbladder, esophagus, stomach, pancreas and duodenum). The code will be publicly available.

翻译：在计算机断层扫描(CT)图像中实现腹部器官的自动分割可支持放射治疗和图像引导手术流程。此类自动解决方案的开发仍面临挑战，主要归因于CT图像中复杂的器官相互作用和模糊边界。为解决这些问题，我们聚焦于有效的空间上下文建模和显式边缘分割先验。据此，我们提出一种包含四个主要组件的端到端训练三维网络：共享编码器、边缘检测器、带边缘跳跃连接(ESCs)的解码器以及循环特征传播头(RFP-Head)。为捕获大范围空间依赖关系，RFP-Head通过有向无环图(DAGs)以高效切片方式传播和采集局部特征——该图利用循环连接根据图像单元的空间排列进行构建。为利用边缘信息，边缘检测器通过边缘监督利用编码器中间特征学习专用于语义分割的边缘先验知识。随后ESCs将边缘知识与多层级解码器特征聚合，学习判别性特征层次结构，显式建模器官内部与边缘在分割中的互补性。我们在两个包含八种标注器官的挑战性腹部CT数据集上开展广泛实验。实验结果表明，所提网络在多个细分结构（胆囊、食管、胃、胰腺和十二指肠）的分割性能上优于多项现有先进模型。相关代码将公开提供。