用单阶段扩散模型生成带标签的组织切片图像，解决细胞分割数据少的问题。
HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification
- 用潜空间扩散模型联合学习细胞布局、分类图与图像分布
- 在Conic和CytoDArk0数据集上生成真实多样样本，提升稀有细胞分类效果
- 适合需要增强标注数据的病理图像分析研究者
精确分割和分类细胞实例对分析组织微环境至关重要，支持医学诊断、预后评估、治疗规划及脑细胞结构研究。然而，高质量标注数据集的构建仍是主要挑战。本文提出一种新型单阶段方法（HistoSmith），通过条件潜空间扩散模型生成图像-标签对以扩充组织病理学数据集。与现有分离生成标签与图像的方法不同，本方法利用潜空间扩散模型学习细胞布局、分类掩码与组织图像的联合分布，可基于用户定义的细胞类型、数量和组织类型进行定制化生成。模型在Conic H&E组织病理学数据集和Nissl染色的CytoDArk0数据集上训练，生成了逼真且多样的标注样本。实验结果表明，该方法显著提升了细胞实例分割与分类性能，尤其改善了Conic数据集中中性粒细胞等稀有细胞类别的表现。研究证实该方法在应对数据稀缺问题方面具有巨大潜力。
原文摘要 · Abstract (English)
Precise segmentation and classification of cell instances are vital for analyzing the tissue microenvironment in histology images, supporting medical diagnosis, prognosis, treatment planning, and studies of brain cytoarchitecture. However, the creation of high-quality annotated datasets for training remains a major challenge. This study introduces a novel single-stage approach (HistoSmith) for generating image-label pairs to augment histology datasets. Unlike state-of-the-art methods that utilize diffusion models with separate components for label and image generation, our approach employs a latent diffusion model to learn the joint distribution of cellular layouts, classification masks, and histology images. This model enables tailored data generation by conditioning on user-defined parameters such as cell types, quantities, and tissue types. Trained on the Conic H&E histopathology dataset and the Nissl-stained CytoDArk0 dataset, the model generates realistic and diverse labeled samples. Experimental results demonstrate improvements in cell instance segmentation and classification, particularly for underrepresented cell types like neutrophils in the Conic dataset. These findings underscore the potential of our approach to address data scarcity challenges.
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