Abstract
The de-identification (deID) of protected health information (PHI) and personally identifiable information (PII) is a fundamental requirement for sharing medical images, particularly through public repositories, to ensure compliance with patient privacy laws. In addition, preservation of non-PHI metadata to inform and enable downstream development of imaging artificial intelligence (AI) is an important consideration in biomedical research. The goal of MIDI-B was to provide a standardized platform for benchmarking of DICOM image deID tools based on a set of rules conformant to the HIPAA Safe Harbor regulation, the DICOM Attribute Confidentiality Profiles, and best practices in preservation of research-critical metadata, as defined by The Cancer Imaging Archive (TCIA). The challenge employed a large, diverse, multi-center, and multi-modality set of real de-identified radiology images with synthetic PHI/PII inserted. The MIDI-B Challenge consisted of three phases: training, validation, and test. Eighty individuals registered for the challenge. In the training phase, we encouraged participants to tune their algorithms using their in-house or public data. The validation and test phases utilized the DICOM images containing synthetic identifiers (of 216 and 322 subjects, respectively). Ten teams successfully completed the test phase of the challenge. To measure success of a rule-based approach to image deID, scores were computed as the percentage of correct actions from the total number of required actions. The scores ranged from 97.91% to 99.93%. Participants employed a variety of open-source and proprietary tools with customized configurations, large language models, and optical character recognition (OCR). In this paper we provide a comprehensive report on the MIDI-B Challenge's design, implementation, results, and lessons learned.
Abstract (translated)
保护健康信息（PHI）和个人识别信息（PII）的去标识化是分享医学影像资料，特别是通过公共存储库进行分享的基本要求，以确保符合患者隐私法的规定。此外，在生物医学研究中保留非PHI元数据，以便促进和助力下游成像人工智能（AI）的发展，也是重要的考量因素之一。 MIDI-B项目的目的是提供一个标准化的平台，用于根据符合HIPAA安全港法规、DICOM属性保密配置文件以及由癌症影像档案馆（TCIA）定义的研究关键元数据保存最佳实践的一套规则来对DICOM图像去标识化工具进行基准测试。挑战赛采用了大量多中心和多种模态的真实放射学脱敏图像，并插入了合成的PHI/PII。 MIDI-B挑战赛包括三个阶段：训练、验证和测试。共有80名参与者注册参加了该挑战赛。在训练阶段，我们鼓励参赛者使用他们自己的内部数据或公开数据来调整他们的算法。验证和测试阶段分别利用包含216名和322名患者合成标识符的DICOM图像进行。 十支队伍成功完成了挑战赛的测试阶段。为了衡量基于规则的方法在影像去标识化中的效果，得分计算为正确操作数占总需执行操作数的百分比。分数范围从97.91%到99.93%不等。参赛者采用了各种开源和专有工具（带有定制配置）、大型语言模型以及光学字符识别（OCR）技术。 本文提供了关于MIDI-B挑战赛的设计、实施、结果及经验教训的全面报告。
URL
https://arxiv.org/abs/2507.23608
