标题： 一种在MIDI-B挑战中的DICOM图像去标识算法 显示英文标题

标题： A DICOM Image De-identification Algorithm in the MIDI-B Challenge

摘要： 图像去标识化对于医疗图像的公开共享至关重要，尤其是在广泛使用的医学数字成像和通信（DICOM）格式中，这符合各种法规和标准的要求，包括健康保险可携性和责任法案（HIPAA）隐私规则、DICOM PS3.15标准以及癌症成像档案（TCIA）推荐的最佳实践。 在第27届国际医学图像计算与计算机辅助干预会议（MICCAI 2024）上组织了医学图像去标识化基准（MIDI-B）挑战赛，旨在使用大量临床DICOM图像数据集来评估基于规则的DICOM图像去标识化算法。 在本报告中，我们探讨了去标识化DICOM图像的关键挑战，强调了移除个人可识别信息（PII）的重要性，以保护患者隐私，同时确保医疗数据在研究、诊断和治疗中的持续可用性，并提供了对此过程进行规范的标准和法规的全面概述。 此外，我们详细描述了在测试阶段处理数据集时应用的去标识化方法——如像素屏蔽、日期偏移、日期哈希、文本识别、文本替换和文本删除——并严格遵守这些标准。 根据MIDI-B挑战赛的最终排行榜，我们最新版本的解决方案算法正确执行了99.92%的必要操作，并在完成挑战的10支队伍中排名第二（总共有22支注册队伍）。 最后，我们对产生的统计数据进行了深入分析，并讨论了当前方法的局限性以及未来改进的潜在途径。 显示英文摘要

摘要： Image de-identification is essential for the public sharing of medical images, particularly in the widely used Digital Imaging and Communications in Medicine (DICOM) format as required by various regulations and standards, including Health Insurance Portability and Accountability Act (HIPAA) privacy rules, the DICOM PS3.15 standard, and best practices recommended by the Cancer Imaging Archive (TCIA). The Medical Image De-Identification Benchmark (MIDI-B) Challenge at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024) was organized to evaluate rule-based DICOM image de-identification algorithms with a large dataset of clinical DICOM images. In this report, we explore the critical challenges of de-identifying DICOM images, emphasize the importance of removing personally identifiable information (PII) to protect patient privacy while ensuring the continued utility of medical data for research, diagnostics, and treatment, and provide a comprehensive overview of the standards and regulations that govern this process. Additionally, we detail the de-identification methods we applied - such as pixel masking, date shifting, date hashing, text recognition, text replacement, and text removal - to process datasets during the test phase in strict compliance with these standards. According to the final leaderboard of the MIDI-B challenge, the latest version of our solution algorithm correctly executed 99.92% of the required actions and ranked 2nd out of 10 teams that completed the challenge (from a total of 22 registered teams). Finally, we conducted a thorough analysis of the resulting statistics and discussed the limitations of current approaches and potential avenues for future improvement.