Abstract #3520

Region of Interest Localization in Large 3D Medical Volumes by Deep Voting

Marc Fischer^1,2, Tobias Hepp³, Ulrich Plabst², Bin Yang², Mike Notohamiprodjo³, and Fritz Schick¹

¹Section on Experimental Radiology, Department of Radiology, University Hospital Tübingen, Tübingen, Germany, ²Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, ³Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany

Identifying Regions of Interest (ROI) such as anatomical landmarks, bounding boxes around organs, certain Field of Views or the selection of a particular body region is of increasing relevance for fully automated analysis pipelines of large cohort imaging data. In this work, a 3D Deep Voting approach based on recent advancements in the field of Deep Learning is proposed, which is able to locate ROIs including single points like anatomical landmarks as well as planes to identify region separators within 3D MRI and CT datasets.

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