发表机构
Fraunhofer Institute for Digital Medicine MEVIS; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital; Harvard Medical School; PixelMed Publishing; Radical Imaging LLC; Massachusetts General Hospital; Stanford University; University Hospital Erlangen; Stony Brook University; National Institute of Allergy and Infectious Diseases; Google Research; Brigham and Women's Hospital（弗劳恩霍夫数字医学MEVIS研究所; 马萨诸塞州总医院阿西诺拉·A·马丁诺斯生物医学成像中心; 哈佛医学院; PixelMed出版公司; Radical成像有限责任公司; 马萨诸塞州总医院; 斯坦福大学; 埃尔朗根大学医院; 石溪大学; 美国国家过敏和传染病研究所; 谷歌研究院; 布莱根和妇女医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出利用DICOM标准在NCI成像数据共享平台中标准化编码和共享病理学图像衍生数据，通过转换五个代表性数据集并公开共享，展示了协调优势及开源工具贡献，以促进该领域的更广泛采用。
AI 中文摘要
计算病理学方法的开发和评估需要访问大型且多样化的数据集。在过去十年中，各种举措在收集、集中和共享病理学成像数据方面投入了大量资源。相比之下，图像衍生数据（如感兴趣区域勾画或分割掩膜）的共享发展尚不完善。在这项工作中，我们描述了在美国国家癌症研究所（NCI）成像数据共享平台（IDC）中以标准化方式编码和共享病理学图像衍生数据的方法，该平台托管并提供去标识化的放射学和病理学数据的公共访问。IDC依赖医学数字成像和通信（DICOM）标准进行数据协调，然而，迄今为止，DICOM在病理学图像衍生内容中的应用在很大程度上尚未被探索。在此，我们展示了五个代表性数据集，这些数据集通过将其原始表示转换为DICOM进行协调，并在IDC中公开共享。我们展示了这种协调的好处，描述了对关键开源工具的重要贡献，并讨论了与更广泛采用DICOM用于病理学图像衍生数据相关的技术考量。
英文摘要
Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into collecting, centralizing, and sharing pathology imaging data. In contrast, sharing of image-derived data such as region-of-interest delineations or segmentation masks is less well developed. In this work, we describe our approach to encoding and sharing image-derived pathology data in a standardized manner within the National Cancer Institute (NCI) Imaging Data Commons (IDC), a platform that hosts and provides public access to de-identified radiology and pathology data. The IDC relies on the Digital Imaging and Communications in Medicine (DICOM) standard for data harmonization, yet the adoption of DICOM for pathology image-derived content has remained largely unexplored until now. Here, we present five representative datasets harmonized by conversion from their original representations into DICOM and shared publicly in the IDC. We demonstrate the benefits of this harmonization, describe contributions to critical open-source tooling, and discuss technical considerations relevant to broader adoption of DICOM for pathology image-derived data.
CommentsDaniela P. Schacherer, Christopher P. Bridge: contributed equally
