Radiomic Medical Data Transformation for Radiologists Support
Marcin Luckner, Paweł Gelar, Agata Kaczmarek, and 5 more authors
In Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings), 2025
Poster
We present a process of transforming medical data into a system that supports radiologists’ interpretation and understanding of Computed Tomography (CT) images. The system is based on a pipeline that includes image conversion, organ segmentation, feature extraction, and report rendering. The final report presents organ visualisations and information about organ measurements, with marked outliers, to the radiologist. The system was created using data from the database containing over 40,000 CT scans and a pre-trained Swin UNETR architecture. The system obtained 89.09% DICE for five segmented organs. The created solution can go through the process in less than five and a half minutes, and its usability was confirmed by radiologists.