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) module 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 https://github.com/zhangye-zoe/SEINE.

翻译：病理组织图像中的细胞核实例分割对生物分析与癌症诊断至关重要，但存在两大挑战：（1）嗜色细胞核的核内与核外区域视觉表现相似，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。针对上述问题，本文提出结构编码与交互网络SEINE，通过构建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的关联性，实现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（SGA）模块，以清晰细胞核为原型增强模糊核的结构学习。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（PE）方法抑制错误的细胞核边界预测。大量实验表明本方法的优越性，SEINE在四个数据集上达到最先进（SOTA）性能。代码开源地址：https://github.com/zhangye-zoe/SEINE。