  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.
