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.

翻译：本报告探讨了人体受试者及生物样本医学图像去标识化的技术问题，旨在将涉及伦理、道德及法律关切的重标识风险充分降低，使得无论来源及传播地的司法管辖区如何，均可为任何目的进行不受限制的公开共享。所有医学图像皆在考虑范围内，无论其采集方式，但重点涉及附带数据元素的图像，尤其是以数据元素嵌入格式编码的图像，特别是医学数字成像与通信（DICOM）格式。这些图像包括分割、参数图及放射治疗（RT）剂量对象等类图像对象。范围亦涵盖相关非图像对象，如RT结构集、计划与剂量体积直方图、结构化报告及呈现状态。本报告仅考虑公开数据发布的去标识化，替代性隐私保护方法（如用于人工智能（AI）模型开发的联邦学习）及AI模型共享导致的隐私泄露问题均不在讨论范围内。仅处理公开共享的技术问题。