TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
Journal:
arXiv
Published Date:
Dec 8, 2024
Abstract
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 Frechet 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.
