基于解剖亚区的解剖切除（AR）是一种精确手术切除的有前途的方法，已经被证明可以通过减少局部复发来改善长期生存率。器官手术解剖结构的细粒度分割（FGS-OSA），即将器官分割成多个解剖区域，对于AR手术计划中肿瘤的定位至关重要。然而，在计算机辅助方法中自动获取FGS-OSA结果面临着外观模糊性挑战，因为器官手术解剖结构的不同亚区具有相似的HU分布、不可见的界限以及解剖标志物和其他解剖信息的相似之处。本文提出了一种新颖的细粒度分割框架，称为“解剖关系推理图卷积网络”（ARR-GCN），将先前的解剖关系纳入框架学习中。在ARR-GCN中，一个基于子区域的图被构造出来，以建模类和它们的关系。此外，为了获取图空间的判别性初始节点表示，设计了一个子区域中心模块。最重要的是，为了明确学习解剖关系，将亚区之间的先前解剖关系编码成邻接矩阵的形式，并将其嵌入到中间节点表示中，以引导框架学习。ARR-GCN在两个FGS-OSA任务上进行验证：i）肝脏分段分割，和ii）肺叶分割。在这两个任务的实验结果中，ARR-GCN的表现优于其他最先进的分割方法，并通过抑制亚区之间的模糊性展现出有前途的性能。

Anatomical resection (AR) based on anatomical sub-regions is a promising method of precise surgical resection, which has been proven to improve long-term survival by reducing local recurrence. The fine-grained segmentation of an organ's surgical anatomy (FGS-OSA), i.e., segmenting an organ into multiple anatomic regions, is critical for localizing tumors in AR surgical planning. However, automatically obtaining FGS-OSA results in computer-aided methods faces the challenges of appearance ambiguities among sub-regions (i.e., inter-sub-region appearance ambiguities) caused by similar HU distributions in different sub-regions of an organ's surgical anatomy, invisible boundaries, and similarities between anatomical landmarks and other anatomical information. In this paper, we propose a novel fine-grained segmentation framework termed the "anatomic relation reasoning graph convolutional network" (ARR-GCN), which incorporates prior anatomic relations into the framework learning. In ARR-GCN, a graph is constructed based on the sub-regions to model the class and their relations. Further, to obtain discriminative initial node representations of graph space, a sub-region center module is designed. Most importantly, to explicitly learn the anatomic relations, the prior anatomic-relations among the sub-regions are encoded in the form of an adjacency matrix and embedded into the intermediate node representations to guide framework learning. The ARR-GCN was validated on two FGS-OSA tasks: i) liver segments segmentation, and ii) lung lobes segmentation. Experimental results on both tasks outperformed other state-of-the-art segmentation methods and yielded promising performances by ARR-GCN for suppressing ambiguities among sub-regions.