Through yearly updates, Materialise Mimics Innovation Suite (MIS) strives to position itself as a tool better equipped to meet today’s challenges surrounding the processing of sensitive information. With MIS 21.0, we present an entirely rewritten Anonymize tool, which offers users the option to de-identify sensitive information of data subjects according to the needs and requirements of their company or organization.
Materialise Mimics 21.0 supports the de-identification of both Mimics project files (.mcs) and DICOM® image files applying the rewritten Anonymize tool and several Python APIs respectively. As such, users can select the de-identification feature that fits their needs best in regard to the processing of sensitive information of data subjects.
Standards-based approach of de-identification medical images
The updated Mimics Innovation Suite 21.0 tools to de-identify individually identifiable information of data subjects follow the international DICOM® standard, and more specifically the DICOM® PS3.15 2018a – Security and System Management Profiles (Appendix E).
This so-called ‘Basic Application Level Confidentiality Profile’ (BALCP) is intended for use in “scenarios in which de-identification may be required, such as creation of teaching files, other types of publication, as well as submission of images and associated information to registries, such as oncology or radiation dose registries”.
Additionally, the DICOM® standard provides a number options to be applicable to the Basic Application Level Confidentiality Profile when scenarios “require removal of additional information” or “require retention of information that would otherwise be removed”.
From single .mcs file to batch de-identification
Our rewritten Anonymize tool targeting Mimics project files (.mcs) applies the BALCP by default. In accordance with the DICOM® standard, checkboxes in the tool allow users to choose from various ‘retain’ and ‘clean’ options as stipulated by the above-mentioned PS3.15 2016b standard (Appendix E).
The Anonymize tool included in the Mimics Base module is relevant for projects which require the de-identification of one .mcs file at the time. To de-identify multiple .mcs project files at once, we’ve equipped the scripting module with Python APIs as to create the possibility to, for example, de-identify past datasets intended for, for example, trials or publications.
In addition to supporting the de-identification of Mimics project files, these Python APIs also support the de-identification of DICOM® image files directly. As is the case with the Anonymize tool, users can determine for every individual DICOM® tag whether they want to use the proposed anonymization or replace the tag with a custom-defined value instead. An interesting feature if, for example, one wishes to store a custom anonymized patient ID.
The newest update of Mimics Innovation Suite will be rolled out in June, but we’re more than happy to show you its possibilities today. Our webinar series, for example, are an excellent way to discover how experts implement MIS in their daily workflow to achieve optimal results. Find out more here!