Localization and Classification of Teeth in Cone Beam CT using Convolutional Neural Networks

accepted for publication
  • In dentistry, software-based medical image analysis and visualization provide efficient 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 dimensionality reduction through non-planar reformatting of the jaw anatomy, DVT data can be efficiently 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 distance to ground truth labels.
Metadaten
Author:Mario Neumann, Olaf Hellwich, Stefan ZachowORCiD
Document Type:Article
Parent Title (English):Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC)
Year of first publication:2019