用多模型聚合提升14类肿瘤微环境成分的病理图像分割精度
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis
- 通过聚合多个分割模型，融合组织细胞的层级结构信息
- 在多种组织类型和机构数据上实现14类TME成分的快速精准分割
- 适合需要定量分析肿瘤微环境的临床与科研人员使用
肿瘤微环境（TME）在癌症进展和治疗反应中起关键作用，但现有对H&E染色组织切片的全面分析方法在细胞类型多样性和准确性方面存在显著局限。本文提出PAGET（基于聚合教师的病理图像分割），一种新型知识蒸馏方法，整合多个分割模型并考虑TME中细胞类型的层次结构。借助通过免疫组化复染技术构建的独特数据集及现有分割模型，PAGET可同时识别与分类14个关键TME组分。我们验证了PAGET在多种组织类型和医疗机构数据上的快速、全面分割能力，推动了肿瘤微环境的定量分析。该方法显著提升了对癌症生物学的理解，并支持从大规模组织病理图像中进行精准临床决策。
原文摘要 · Abstract (English)
The tumor microenvironment (TME) plays a crucial role in cancer progression and treatment response, yet current methods for its comprehensive analysis in H&E-stained tissue slides face significant limitations in the diversity of tissue cell types and accuracy. Here, we present PAGET (Pathological image segmentation via AGgrEgated Teachers), a new knowledge distillation approach that integrates multiple segmentation models while considering the hierarchical nature of cell types in the TME. By leveraging a unique dataset created through immunohistochemical restaining techniques and existing segmentation models, PAGET enables simultaneous identification and classification of 14 key TME components. We demonstrate PAGET's ability to perform rapid, comprehensive TME segmentation across various tissue types and medical institutions, advancing the quantitative analysis of tumor microenvironments. This method represents a significant step forward in enhancing our understanding of cancer biology and supporting precise clinical decision-making from large-scale histopathology images.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。