Lianas Luca, Del Rio Mauro, Pireddu Luca, Aspegren Oskar, Giunchi Francesca, Fiorentino Michelangelo, Leo Simone, Zelic Renata, Vincent Per Henrik, Destefanis Nicolas, Zugna Daniela, Richiardi Lorenzo, Pettersson Andreas, Akre Olof, Frexia Francesca
Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy.
Department of Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden.
Sci Rep. 2025 Aug 7;15(1):28910. doi: 10.1038/s41598-025-13546-7.
The rapid evolution of digital pathology has enabled large-scale data acquisition, driving sophisticated clinical research and advancing the development of AI-driven tools. These innovations have also revolutionised histopathological slide review, especially the annotation step (i.e. the process of marking specific areas of interest on glass-mounted tissue samples to add relevant clinical information) by digitising the process, enhancing precision and efficiency, and facilitating collaboration. However, currently available open-source annotation tools typically employ single-label approaches that provide a flat representation of whole-slide images (WSI), limiting their ability to capture the complexity of the diagnosis-significant elements in a detailed and structured way. Furthermore, the difficulty of strictly following precise review protocols and lack of provenance tracking during annotation processes can result in high variability and limit reproducibility and reusability of the collected data. In this work we present the CRS4 Digital Pathology Platform (CDPP), an open-source system for research studies that manages WSI collections and focuses on high-quality, structured annotations, gathered according to well-defined protocols. Its main features include: (1) structured, multi-label morphological and clinical image annotation; (2) support for controlled but customisable annotation protocols; (3) dedicated annotation tools to facilitate enhanced accuracy, efficiency and consistency in the annotation process; and (4) workflow-based computational analysis with integrated provenance tracking. We show how the platform has successfully supported three different studies, demonstrating the CDPP's ability to assist pathologists in the generation of high-quality annotated datasets, also suitable for reuse, in the context of digital pathology research.
数字病理学的快速发展使得大规模数据采集成为可能，推动了精密的临床研究，并促进了人工智能驱动工具的开发。这些创新还彻底改变了组织病理学玻片检查，尤其是通过将注释步骤（即在玻璃载片组织样本上标记特定感兴趣区域以添加相关临床信息的过程）数字化，提高了精度和效率，并促进了协作。然而，目前可用的开源注释工具通常采用单标签方法，这种方法提供了全玻片图像（WSI）的平面表示，限制了它们以详细且结构化的方式捕捉诊断重要元素复杂性的能力。此外，在注释过程中严格遵循精确审查协议的困难以及缺乏来源跟踪可能导致高变异性，并限制所收集数据的可重复性和可重用性。在这项工作中，我们展示了CRS4数字病理学平台（CDPP），这是一个用于研究的开源系统，用于管理WSI集合，并专注于根据明确协议收集的高质量、结构化注释。其主要功能包括：（1）结构化、多标签形态学和临床图像注释；（2）支持可控但可定制的注释协议；（3）专用注释工具，以促进注释过程中的准确性、效率和一致性；以及（4）基于工作流程的计算分析与集成的来源跟踪。我们展示了该平台如何成功支持三项不同的研究，证明了CDPP在数字病理学研究背景下协助病理学家生成高质量注释数据集（也适用于重用）的能力。
