A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at multiple magnifications. These images are digitally viewable, analyzable, and shareable, and are widely used for Artificial Intelligence (AI) algorithm development. WSIs play an important role in pathology for disease diagnosis and oncology for cancer research, but are also applied in neurology, veterinary medicine, hematology, microbiology, dermatology, pharmacology, toxicology, immunology, and forensic science. When assembling cohorts for AI training or validation, it is essential to know the content of a WSI. However, no standard currently exists for this metadata, and such a selection has largely relied on manual inspection, which is not suitable for large collections with millions of objects. We propose a general framework to generate 2D index maps (tissue maps) that describe the morphological content of WSIs using common syntax and semantics to achieve interoperability between catalogs. The tissue maps are structured in three layers: source, tissue type, and pathological alterations. Each layer assigns WSI segments to specific classes, providing AI-ready metadata. We demonstrate the advantages of this standard by applying AI-based metadata extraction from WSIs to generate tissue maps and integrating them into a WSI archive. This integration enhances search capabilities within WSI archives, thereby facilitating the accelerated assembly of high-quality, balanced, and more targeted datasets for AI training, validation, and cancer research.

翻译：全玻片图像（Whole Slide Image, WSI）是通过以多种放大倍数扫描包含生物样本（如组织切片或细胞样本）的整个玻璃玻片而生成的高分辨率数字图像。这些图像可进行数字化查看、分析和共享，并广泛用于人工智能（AI）算法开发。WSI在病理学疾病诊断和肿瘤学癌症研究中发挥着重要作用，同时也应用于神经学、兽医学、血液学、微生物学、皮肤病学、药理学、毒理学、免疫学及法医学等领域。在为AI训练或验证组建队列时，了解WSI的内容至关重要。然而，目前尚无此类元数据的标准，此类筛选主要依赖人工检查，这不适用于包含数百万对象的大规模数据集。我们提出了一个通用框架，用于生成描述WSI形态学内容的二维索引图谱（组织图谱），该框架采用通用语法和语义，以实现不同目录间的互操作性。组织图谱结构分为三层：来源、组织类型和病理改变。每一层都将WSI片段分配至特定类别，从而提供AI就绪的元数据。我们通过应用基于AI的WSI元数据提取来生成组织图谱，并将其集成到WSI档案库中，以此展示该标准的优势。这种集成增强了WSI档案库内的检索能力，从而有助于为AI训练、验证及癌症研究加速组建高质量、均衡且更具针对性的数据集。