标题： 用于训练和基准测试H&E切片中乳腺癌分割的多中心数据集 显示英文标题

标题： A Multicentric Dataset for Training and Benchmarking Breast Cancer Segmentation in H&E Slides

摘要： 全自动全幻灯片图像（WSIs）的语义分割，使用苏木精和伊红（H&E）染色，在乳腺癌的大规模基于人工智能的生物标志物分析中是必不可少的。 然而，现有的乳腺癌分割公共数据集缺乏支持模型泛化性和在异质患者队列中稳健生物标志物验证所需的形态多样性。 我们引入了BrEast cancEr hisTopathoLogy sEgmentation（BEETLE），一个用于H&E染色乳腺癌WSIs多类语义分割的数据集。 它包含来自三个合作临床中心和两个公共数据集的587个活检和切除样本，使用七种扫描仪数字化，并涵盖了所有分子亚型和组织学等级。 通过多样化的注释策略，我们收集了四个类别的注释——浸润性上皮、非浸润性上皮、坏死和其他——特别关注现有数据集中代表性不足的形态，如原位导管癌和分散的lobular肿瘤细胞。 该数据集的多样性和对乳腺癌自动化生物标志物定量迅速增长领域的相关性，确保了其高重用潜力。 最后，我们提供了一个精心整理的多中心外部评估集，以实现乳腺癌分割模型的标准基准测试。 显示英文摘要

摘要： Automated semantic segmentation of whole-slide images (WSIs) stained with hematoxylin and eosin (H&E) is essential for large-scale artificial intelligence-based biomarker analysis in breast cancer. However, existing public datasets for breast cancer segmentation lack the morphological diversity needed to support model generalizability and robust biomarker validation across heterogeneous patient cohorts. We introduce BrEast cancEr hisTopathoLogy sEgmentation (BEETLE), a dataset for multiclass semantic segmentation of H&E-stained breast cancer WSIs. It consists of 587 biopsies and resections from three collaborating clinical centers and two public datasets, digitized using seven scanners, and covers all molecular subtypes and histological grades. Using diverse annotation strategies, we collected annotations across four classes - invasive epithelium, non-invasive epithelium, necrosis, and other - with particular focus on morphologies underrepresented in existing datasets, such as ductal carcinoma in situ and dispersed lobular tumor cells. The dataset's diversity and relevance to the rapidly growing field of automated biomarker quantification in breast cancer ensure its high potential for reuse. Finally, we provide a well-curated, multicentric external evaluation set to enable standardized benchmarking of breast cancer segmentation models.