TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
This work addresses the need for accurate synthetic cell topology generation in digital pathology to enhance tissue structure analysis and data augmentation, representing an incremental advance by refining existing diffusion models with topological constraints.
The paper tackles the problem of generating realistic multi-class cell topologies in digital pathology by integrating topological constraints into a diffusion model, resulting in improved precision for downstream tasks like cell detection and classification, with a new metric (Topological Fréchet Distance) introduced to assess topological fidelity.
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.