TY - THES A1 - Neumann, Mario T1 - Localization and Classification of Teeth in Cone Beam Computed Tomography using 2D CNNs N2 - In dentistry, software-based medical image analysis and visualization provide effcient and accurate diagnostic and therapy planning capabilities. We present an approach for the automatic recognition of tooth types and positions in digital volume tomography (DVT). By using deep learning techniques in combination with dimension reduction through non-planar reformatting of the jaw anatomy, DVT data can be effciently processed and teeth reliably recognized and classified, even in the presence of imaging artefacts, missing or dislocated teeth. We evaluated our approach, which is based on 2D Convolutional Neural Networks (CNNs), on 118 manually annotated cases of clinical DVT datasets. Our proposed method correctly classifies teeth with an accuracy of 94% within a limit of 2mm distancr to ground truth landmarks. KW - Dental Imaging KW - Tooth Classification KW - Dimension Reduction KW - Image Reformatting Y1 - 2019 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/7404 UR - https://nbn-resolving.org/urn:nbn:de:0297-zib-74045 ER -