TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model.

Area of Science:

• Digital Pathology
• Computational Biology
• Medical Imaging

Background:

• Accurate modeling of multi-class cell topology is essential for understanding tissue structure and pathology.
• Synthetic cell topology generation aids in simulating complex tissue environments and augmenting training data.
• Current methods lack precise control and interpretability in generating complex cellular arrangements.

Purpose of the Study:

• To propose a novel approach for generating realistic and contextually accurate multi-class cell topologies.
• To integrate topological constraints into a diffusion model for improved synthetic cell layout generation.
• To enhance the precision and interpretability of cell topology simulations for downstream applications.

Main Methods:

• Integration of topological constraints within a diffusion model framework.
• Refinement of cell distribution and interaction simulations.
• Introduction of a new metric, Topological Fréchet Distance (TopoFD), for evaluating topological fidelity.

Main Results:

• Demonstrated effectiveness in generating multi-class cell layouts with accurate topological relationships.
• Improved precision and interpretability in downstream tasks like cell detection and classification.
• Validation of the proposed method's ability to capture intricate cellular structures.

Conclusions:

• The proposed diffusion model with topological constraints effectively generates realistic multi-class cell topologies.
• The novel TopoFD metric provides a superior evaluation of topological structure compared to traditional metrics.
• This work advances digital pathology by enabling better simulations and analysis of tumor microenvironments.