Improved distinct bone segmentation in upper-body CT through multi-resolution networks

Eva Schnider, Julia Wolleb, Antal Huck, Mireille Toranelli, Georg Rauter, Magdalena Müller-Gerbl, Philippe C. Cattin

Abstract: Automated distinct bone segmentation from CT scans is widely used in planning and navigation workflows. U-Net variants are known to provide excellent results in supervised semantic segmentation. However, in distinct bone segmentation from upper-body CTs a large field of view and a computationally taxing 3D architecture are required. This leads to low-resolution results lacking detail or localisation errors due to missing spatial context when using high-resolution inputs.