De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy
This tool addresses privacy compliance issues for researchers handling medical imaging data under regulations like GDPR and HIPAA, though it is incremental as it builds on existing de-identification techniques.
The authors tackled the challenge of de-identifying sensitive patient health information in medical imaging data by developing an open-source tool that automates anonymization for DICOM MRI, CT, whole slide images, and MR twix raw data, including text removal via a neural network.
Medical data employed in research frequently comprises sensitive patient health information (PHI), which is subject to rigorous legal frameworks such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). Consequently, these types of data must be pseudonymized prior to utilisation, which presents a significant challenge for many researchers. Given the vast array of medical data, it is necessary to employ a variety of de-identification techniques. To facilitate the anonymization process for medical imaging data, we have developed an open-source tool that can be used to de-identify DICOM magnetic resonance images, computer tomography images, whole slide images and magnetic resonance twix raw data. Furthermore, the implementation of a neural network enables the removal of text within the images. The proposed tool automates an elaborate anonymization pipeline for multiple types of inputs, reducing the need for additional tools used for de-identification of imaging data. We make our code publicly available at https://github.com/code-lukas/medical_image_deidentification.