On Efficient Extraction of Pelvis Region from CT Data
Tatyana Ivanovska, Andrian O. Paulus, Robert Martin, Babak Panahi, Arndt Schilling
Georg-August University Göttingen, Department for Computational Neuroscience
The first step in automated analysis of medical volumetric data is to detect slices, where specific body parts are located. In our project, we aimed to extract the pelvis regionfrom whole-body CT scans. Two deep learning approaches, namely, an unsupervised slice score regressor, and a supervised slice classification method, were evaluated on a relatively small-sized dataset. The result comparison showed that both methods could detect the region of interest with accuracy above 93%. Although the straightforward classification method delivered more accurate results (accuracy of 99%), sometimes it tended to output discontinuous regions, which can be solved by combination of both approaches.