Macho, Philipp MartenKurz, NadjaUlges, AdrianBrylka, RobertGietzen, ThomasSchwanecke, Ulrich{Tam, Gary K. L. and Vidal, Franck2018-09-192018-09-192018978-3-03868-071-0https://diglib.eg.org:443/handle/10.2312/cgvc20181213https://doi.org/10.2312/cgvc.20181213This paper addresses the automatic segmentation of teeth in volumetric Computed Tomography (CT) scans of the human skull. Our approach is based on a convolutional neural network employing 3D volumetric convolutions. To tackle data scale issues, we apply a hierarchical coarse-to fine approach combining two CNNs, one for low-resolution detection and one for highresolution refinement. In quantitative experiments on 40 CT scans with manually acquired ground truth, we demonstrate that our approach displays remarkable robustness across different patients and device vendors. Furthermore, our hierarchical extension outperforms a single-scale segmentation, and network size can be reduced compared to previous architectures without loss of accuracy.Computer GraphicsImage processingComputing / Technology PolicyMedical technologiesMachine LearningNeural networks10.2312/cgvc.20181213109-113