Efficient DICOM Image Tagging and Cohort Curation Within Kaapana

Zusammenfassung

The adaptation and application of medical image analysis algorithms inside a clinical environment comes with the challenge of defining, curating and annotating suitable training and testing cohorts from increasing numbers of available DICOM images. Systems for automated image retrieval and cohort selection have emerged in recent years. Commonly, however, a physician still needs to verify results and take a look at the images themselves to take a final decision. In this work, in order to assist this process and to provide functionalities for standard-conform tagging and adding of free-text to DICOM images, we combine two open source tools, namely Doccano and OHIF Medical Imaging Viewer. We integrate them into the Kaapana open source platform and imaging toolkit. We demonstrate how these functionalities can be leveraged to curate cohorts, add, adjust and enrich DICOM metadata and to tag images for image classification or image-text correlation tasks. Having these steps integrated in a DICOM-conform way also represents an important step towards adopting FAIR-principles in the scientific process.