TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model

Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology. The synthetic generation of cell topology enables realistic simulations of complex tissue environments, enhances downstream tasks by augmenting trainin...

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Published in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Vol. 2025; pp. 20979 - 20989
Main Authors: Xu, Meilong, Gupta, Saumya, Hu, Xiaoling, Li, Chen, Abousamra, Shahira, Samaras, Dimitris, Prasanna, Prateek, Chen, Chao
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.06.2025
Subjects:
cell topology generation
Codes
Computer vision
Data models
Diffusion models
digital pathology
Histopathology
Layout
Measurement
Pattern recognition
Topology
Training data
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary:Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology. The synthetic generation of cell topology enables realistic simulations of complex tissue environments, enhances downstream tasks by augmenting training data, aligns more closely with pathologists' domain knowledge, and offers new opportunities for controlling and generalizing the tumor microenvironment. In this paper, we propose a novel approach that integrates topological constraints into a diffusion model to improve the generation of realistic, contextually accurate cell topologies. Our method refines the simulation of cell distributions and interactions, increasing the precision and interpretability of results in downstream tasks such as cell detection and classification. To assess the topological fidelity of generated layouts, we introduce a new metric, Topological Fréchet Distance (TopoFD), which overcomes the limitations of traditional metrics like FID in evaluating topological structure. Experimental results demonstrate the effectiveness of our approach in generating multi-class cell layouts that capture intricate topological relationships. Code is available at https://github.com/Melon-Xu/TopoCellGen.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR52734.2025.01954