Localization and Classification of Teeth in Cone Beam Computed Tomography using 2D CNNs

Please always quote using this URN: urn:nbn:de:0297-zib-74045
  • 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.

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Metadaten
Author:Mario Neumann
Document Type:Master's Thesis
Tag:Dental Imaging; Dimension Reduction; Image Reformatting; Tooth Classification
Granting Institution:Technische Universit├Ąt Berlin
Advisor:Stefan Zachow
Date of final exam:2019/07/31
Year of first publication:2019
Pagenumber:77