Clunie David A, Flanders Adam, Taylor Adam, Erickson Brad, Bialecki Brian, Brundage David, Gutman David, Prior Fred, Seibert J Anthony, Perry John, Gichoya Judy Wawira, Kirby Justin, Andriole Katherine, Geneslaw Luke, Moore Steve, Fitzgerald T J, Tellis Wyatt, Xiao Ying, Farahani Keyvan
ArXiv. 2025 Mar 16:arXiv:2303.10473v3.
This report addresses the technical aspects of de-identification of medical images of human subjects and biospecimens, such that re-identification risk of ethical, moral, and legal concern is sufficiently reduced to allow unrestricted public sharing for any purpose, regardless of the jurisdiction of the source and distribution sites. All medical images, regardless of the mode of acquisition, are considered, though the primary emphasis is on those with accompanying data elements, especially those encoded in formats in which the data elements are embedded, particularly Digital Imaging and Communications in Medicine (DICOM). These images include image-like objects such as Segmentations, Parametric Maps, and Radiotherapy (RT) Dose objects. The scope also includes related non-image objects, such as RT Structure Sets, Plans and Dose Volume Histograms, Structured Reports, and Presentation States. Only de-identification of publicly released data is considered, and alternative approaches to privacy preservation, such as federated learning for artificial intelligence (AI) model development, are out of scope, as are issues of privacy leakage from AI model sharing. Only technical issues of public sharing are addressed.
本报告阐述了对人类受试者医学图像和生物样本进行去标识化处理的技术层面内容，以便将涉及伦理、道德和法律问题的重新识别风险充分降低，从而允许为任何目的进行不受限制的公开共享，无论源站点和分发站点的管辖范围如何。所有医学图像，无论其采集方式如何，均在考虑范围内，不过主要重点是那些带有伴随数据元素的图像，尤其是那些以数据元素嵌入其中的格式编码的图像，特别是医学数字成像和通信（DICOM）格式。这些图像包括类似图像的对象，如分割图、参数图和放射治疗（RT）剂量对象。范围还包括相关的非图像对象，如RT结构集、计划和剂量体积直方图、结构化报告以及呈现状态。仅考虑对公开发布数据的去标识化处理，用于人工智能（AI）模型开发的联合学习等隐私保护替代方法不在范围内，AI模型共享导致的隐私泄露问题也不在范围内。本报告仅涉及公开共享的技术问题。
