Instance-level semantic segmentation of nuclei based on multimodal structure encoding
By deeply mining the morphological features of cell nuclei and their spatial topological relationships, our graph neural structure encoding framework achieves high-precision nuclear segmentation and classification. This approach shows significant potential for enhancing histopathological image analysis, potentially leading to more accurate diagnoses and improved understanding of cellular structures in pathological tissues.
Authors
- Bo Guan (ORCID: https://orcid.org/0000-0002-0936-4172)
- Guangdi Chu (ORCID: https://orcid.org/0000-0002-2963-1741)
- Bo Yi (ORCID: https://orcid.org/0000-0002-8073-8202)
- Jianmin Li (ORCID: https://orcid.org/0000-0001-7491-0320)
- Ziying Wang
Institutions
- Qingdao University (CN)
- Central South University (CN)
- Tianjin University (CN)
- Affiliated Hospital of Qingdao University (CN)
- Third Xiangya Hospital (CN)
- Ministry of Education (KN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-02-06
- DOI
- https://doi.org/10.1186/s12859-025-06066-8
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- National Natural Science Foundation of China