arXiv 2501.029092025-01-06cs.CV

通过教师聚合实现综合病理图像分割以用于肿瘤微环境分析

Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis

The University of Tokyo（东京大学）；Institute of Science Tokyo（东京科学大学）；Tottori University（鸟取大学）；Biomy Inc.（Biomy公司）；Japanese Red Cross Medical Center（日本红十字会医疗中心）；Kanagawa Cancer Center（神奈川县癌症中心）；Kanagawa Cancer Center Research Institute（神奈川县癌症中心研究所）；National Cancer Center Exploratory Oncology Research & Clinical Trial Center（国立癌症中心探索性肿瘤学研究与临床试验中心）

Daisuke Komura, Maki Takao, Mieko Ochi, Takumi Onoyama, Hiroto Katoh, Hiroyuki Abe, Hiroyuki Sano, Teppei Konishi, Toshio Kumasaka, Tomoyuki Yokose, Yohei Miyag… 展开作者

Daisuke Komura, Maki Takao, Mieko Ochi, Takumi Onoyama, Hiroto Katoh, Hiroyuki Abe, Hiroyuki Sano, Teppei Konishi, Toshio Kumasaka, Tomoyuki Yokose, Yohei Miyagi, Tetsuo Ushiku, Shumpei Ishikawa

备注：: 38 pages, 13 figures

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AI总结：

提出知识蒸馏框架PAGET，通过聚合多个教师分割模型并利用免疫组化复染数据集，实现14类肿瘤微环境成分的快速、全面分割。

摘要：

肿瘤微环境（TME）在癌症进展和治疗反应中起着关键作用，然而目前在H&E染色组织切片中对其进行综合分析的方法，在组织细胞类型的多样性和准确性方面面临显著局限。为此，我们提出PAGET（Pathological image segmentation via AGgrEgated Teachers，即通过聚合教师实现病理图像分割），这是一种新的知识蒸馏方法，整合多个分割模型，同时考虑TME中细胞类型的层级性质。借助通过免疫组织化学复染技术创建的独特数据集以及现有分割模型，PAGET能够同时识别和分类14种关键TME成分。我们证明了PAGET有能力在不同组织类型和医疗机构之间进行快速、全面的TME分割，推动肿瘤微环境的定量分析。该方法代表了一项重要进展，有助于增进我们对癌症生物学的理解，并支持从大规模组织病理学图像中进行精确的临床决策。

英文摘要：

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.

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