There are many tools to anonymize the DICOM 'tags' containing the metadata associated with the image, but few also allow the anonymization of the 'pixel data'. If the image includes annotations, the de-identification must also be done by 'cleaning' the pixels of the image. In addition to this process, you must ensure that the image itself does not contain any personal data, especially when annotations are burned into the image (called “ burned in annotations”). You will find details regarding these 'tags' and the various confidentiality profiles recommended by the DICOM PS 3.15 standard: Appendix E in this article. In order to use them in a computer vision project, it is essential that they be anonymized because they contain Personal Health Information (PHI).Īs seen in a previous article, the anonymization or de-identification of DICOM files involves processing information from several 'tags' in the file that may contain personal patient information. Commonly, medical images are stored in DICOM format in the PACS of the health facility where they were collected. CT images, MR images) for a computer vision project using deep learning methods. In a series of articles, we will focus on the data preprocessing of medical images (e.g.

Medical Imaging data preprocessing in AI: An essential step

It consists of several steps that vary depending on the type of project you are going to conduct and the type of data you have. Data preprocessing is an important and mandatory step for any good data scientist to take when embarking on a data analysis project.

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