HistoSmith：基于条件潜扩散的单阶段组织学图像-标签生成以增强细胞分割与分类
HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification
AI总结：
针对组织学图像细胞分割与分类的高质量标注数据集稀缺问题，提出单阶段方法HistoSmith，通过条件潜扩散模型学习细胞布局、分类掩码与图像的联合分布，生成定制化图像-标签对，在Conic和CytoDArk0数据集上训练后提升了分割分类性能，尤其改善了代表性不足细胞类型的表现。
摘要：
细胞实例的精确分割和分类对于分析组织学图像中的组织微环境至关重要，可支持医学诊断、预后、治疗规划以及脑细胞结构研究。然而，创建用于训练的高质量标注数据集仍是一大挑战。本研究提出一种新颖的单阶段方法（HistoSmith）来生成图像-标签对，以扩充组织学数据集。与使用具有标签和图像生成分离组件的扩散模型的最先进方法不同，我们的方法采用潜扩散模型来学习细胞布局、分类掩码和组织学图像的联合分布。该模型通过以细胞类型、数量和组织类型等用户定义参数为条件，实现定制化数据生成。模型在Conic H&E组织病理学数据集和尼氏染色的CytoDArk0数据集上训练，生成真实且多样的带标签样本。实验结果表明，细胞实例分割和分类性能得到提升，尤其是在Conic数据集中中性粒细胞等代表性不足的细胞类型上。这些发现突显了我们的方法在应对数据稀缺挑战方面的潜力。
英文摘要：
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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