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一种用于隐私保护医学图像分析的两阶段去识别过程。

A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis.

作者信息

Shahid Arsalan, Bazargani Mehran H, Banahan Paul, Mac Namee Brian, Kechadi Tahar, Treacy Ceara, Regan Gilbert, MacMahon Peter

机构信息

School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland.

Department of Radiology, Mater Misericordiae University Hospital, D07 R2WY Dublin, Ireland.

出版信息

Healthcare (Basel). 2022 Apr 19;10(5):755. doi: 10.3390/healthcare10050755.

Abstract

Identification and re-identification are two major security and privacy threats to medical imaging data. De-identification in DICOM medical data is essential to preserve the privacy of patients' Personally Identifiable Information (PII) and requires a systematic approach. However, there is a lack of sufficient detail regarding the de-identification process of DICOM attributes, for example, what needs to be considered before removing a DICOM attribute. In this paper, we first highlight and review the key challenges in the medical image data de-identification process. In this paper, we develop a two-stage de-identification process for CT scan images available in DICOM file format. In the first stage of the de-identification process, the patient's PII-including name, date of birth, etc., are removed at the hospital facility using the export process available in their Picture Archiving and Communication System (PACS). The second stage employs the proposed DICOM de-identification tool for an exhaustive attribute-level investigation to further de-identify and ensure that all PII has been removed. Finally, we provide a roadmap for future considerations to build a semi-automated or automated tool for the DICOM datasets de-identification.

摘要

识别和重新识别是医学影像数据面临的两大安全和隐私威胁。DICOM医学数据中的去识别化对于保护患者个人可识别信息(PII)的隐私至关重要,并且需要一种系统的方法。然而,关于DICOM属性的去识别化过程缺乏足够的详细信息,例如,在删除DICOM属性之前需要考虑什么。在本文中,我们首先突出并回顾医学图像数据去识别化过程中的关键挑战。在本文中,我们针对DICOM文件格式的CT扫描图像开发了一个两阶段的去识别化过程。在去识别化过程的第一阶段,使用医院图像存档与通信系统(PACS)中的导出过程,在医院设施处删除患者的PII,包括姓名、出生日期等。第二阶段使用所提出的DICOM去识别化工具进行详尽的属性级调查,以进一步去识别并确保所有PII都已被删除。最后,我们提供了一个未来考量的路线图,以构建一个用于DICOM数据集去识别化的半自动或自动工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9f8e/9141493/cb72bd902dfd/healthcare-10-00755-g0A1.jpg

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