Segmenting Teeth from Volumetric CT Data with a Hierarchical CNN-based Approach

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dc.contributor.authorMacho, Philipp Martenen_US
dc.contributor.authorKurz, Nadjaen_US
dc.contributor.authorUlges, Adrianen_US
dc.contributor.authorBrylka, Roberten_US
dc.contributor.authorGietzen, Thomasen_US
dc.contributor.authorSchwanecke, Ulrichen_US
dc.contributor.editor{Tam, Gary K. L. and Vidal, Francken_US
dc.date.accessioned2018-09-19T15:15:17Z
dc.date.available2018-09-19T15:15:17Z
dc.date.issued2018
dc.description.abstractThis 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.en_US
dc.description.sectionheadersShort Papers
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20181213
dc.identifier.isbn978-3-03868-071-0
dc.identifier.pages109-113
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20181213
dc.identifier.urihttps://doi.org/10.2312/cgvc.20181213
dc.publisherThe Eurographics Associationen_US
dc.subjectComputer Graphics
dc.subjectImage processing
dc.subjectComputing / Technology Policy
dc.subjectMedical technologies
dc.subjectMachine Learning
dc.subjectNeural networks
dc.titleSegmenting Teeth from Volumetric CT Data with a Hierarchical CNN-based Approachen_US
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