用可定制参数自动识别并去除DICOM医学图像中的隐私信息
Deep classification algorithm for De-identification of DICOM medical images
- 基于HIPAA安全港方法，通过可配置参数分类识别敏感标签
- 成功识别姓名、病史、个人数据等关键隐私信息
- 支持多语言和场景，适合科研与日常医疗数据处理
DICOM文件的去标识化是医学影像研究中的关键环节。根据《健康保险流通与责任法案》（HIPAA）及隐私规定，个人身份信息（PII）和健康相关信息（PHI）必须隐藏或移除，包括全脸照片及其类似图像。本研究旨在实现对DICOM文件头部信息及像素数据中嵌入的PII和PHI进行去标识化。采用基于HIPAA安全港方法的算法，通过用户自定义参数对DICOM标签进行分类，并决定是否去标识。结果表明，姓名、病史、个人资料及机构信息等敏感内容被有效识别。我们开发了基于Python的去标识化算法，其灵活性允许根据具体需求（如语言）定制流程，适用于日常使用和研究场景。代码已开源：https://github.com/rtdicomexplorer/deep_deidentification。
原文摘要 · Abstract (English)
Background : De-identification of DICOM (Digital Imaging and Communi-cations in Medicine) files is an essential component of medical image research. Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHI) need to be hidden or removed due to legal reasons. According to the Health Insurance Portability and Accountability Act (HIPAA) and privacy rules, also full-face photographic images and any compa-rable images are direct identifiers and are considered protected health information that also need to be de-identified. Objective : The study aimed to implement a method that permit to de-identify the PII and PHI information present in the header and burned on the pixel data of DICOM. Methods : To execute the de-identification, we implemented an algorithm based on the safe harbor method, defined by HIPAA. Our algorithm uses input customizable parameter to classify and then possibly de-identify individual DICOM tags. Results : The most sensible information, like names, history, personal data and institution were successfully recognized. Conclusions : We developed a python algorithm that is able to classify infor-mation present in a DICOM file. The flexibility provided by the use of customi-zable input parameters, which allow the user to customize the entire process de-pending on the case (e.g., the language), makes the entire program very promis-ing for both everyday use and research purposes. Our code is available at https://github.com/rtdicomexplorer/deep_deidentification.
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