Nuclei instance segmentation in histopathological images is of great importance for biological analysis and cancer diagnosis but remains challenging for two reasons. (1) Similar visual presentation of intranuclear and extranuclear regions of chromophobe nuclei often causes under-segmentation, and (2) current methods lack the exploration of nuclei structure, resulting in fragmented instance predictions. To address these problems, this paper proposes a structure encoding and interaction network, termed SEINE, which develops the structure modeling scheme of nuclei and exploits the structure similarity between nuclei to improve the integrality of each segmented instance. Concretely, SEINE introduces a contour-based structure encoding (SE) that considers the correlation between nuclei structure and semantics, realizing a reasonable representation of the nuclei structure. Based on the encoding, we propose a structure-guided attention (SGA) that takes the clear nuclei as prototypes to enhance the structure learning for the fuzzy nuclei. To strengthen the structural learning ability, a semantic feature fusion (SFF) is presented to boost the semantic consistency of semantic and structure branches. Furthermore, a position enhancement (PE) method is applied to suppress incorrect nuclei boundary predictions. Extensive experiments demonstrate the superiority of our approaches, and SEINE achieves state-of-the-art (SOTA) performance on four datasets. The code is available at \href{https://github.com/zhangye-zoe/SEINE}{https://github.com/zhangye-zoe/SEINE}.

翻译：组织病理图像中的细胞核实例分割对生物分析和癌症诊断具有重要意义，但仍面临两大挑战：（1）嫌色细胞核的核内与核外区域在视觉表现上高度相似，常导致欠分割问题；（2）现有方法缺乏对细胞核结构的探索，造成实例预测碎片化。针对这些问题，本文提出一种结构编码与交互网络（SEINE），通过建立细胞核结构建模方案并利用核间结构相似性，提升每个分割实例的完整性。具体而言，SEINE引入基于轮廓的结构编码（SE），该编码考虑了细胞核结构与语义之间的关联，实现了对细胞核结构的合理表征。基于该编码，我们提出结构引导注意力机制（SGA），以清晰细胞核为原型增强模糊细胞核的结构学习。为强化结构学习能力，提出语义特征融合（SFF）方法提升语义分支与结构分支的语义一致性。此外，采用位置增强方法（PE）抑制错误的细胞核边界预测。大量实验证明了本方法的优越性，SEINE在四个数据集上均达到最佳性能。代码已开源在https://github.com/zhangye-zoe/SEINE。