用扩散模型生成更真实的病理细胞拓扑结构
TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
- 将拓扑约束融入扩散模型，提升细胞布局生成精度
- 新指标TopoFD有效评估细胞拓扑结构真实性
- 适合需要可控肿瘤微环境的病理研究者
精准建模多类细胞拓扑结构对数字病理学至关重要，可揭示组织结构与病理特征。通过合成生成细胞拓扑，能实现复杂组织环境的逼真模拟，增强下游任务的数据增广效果，更贴近病理科医生的专业知识，并为控制和泛化肿瘤微环境提供新可能。本文提出一种新方法，将拓扑约束引入扩散模型，以提升真实、上下文准确的细胞拓扑生成能力。该方法优化了细胞分布与相互作用的模拟，提高了细胞检测与分类等下游任务的结果精度与可解释性。为评估生成布局的拓扑保真度，我们引入新指标——拓扑弗雷歇距离（Topological Frechet Distance, TopoFD），克服了传统指标（如FID）在评估拓扑结构方面的局限。实验结果表明，本方法能生成捕捉复杂拓扑关系的多类细胞布局。代码已开源：https://github.com/Melon-Xu/TopoCellGen。
原文摘要 · Abstract (English)
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.
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