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  4. Automatic teeth segmentation in panoramic X-ray images using a coupled shape model in combination with a neural network
 
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2018
Conference Paper
Title

Automatic teeth segmentation in panoramic X-ray images using a coupled shape model in combination with a neural network

Abstract
Dental panoramic radiographs depict the full set of teeth in a single image and are used by dentists as a popular first tool for diagnosis. In order to provide the dentist with automatic diagnostic support, a robust and accurate segmentation of the individual teeth is required. However, poor image quality of panoramic x-ray images like low contrast or noise as well as teeth variations in between patients make this task difficult. In this paper, a fully automatic approach is presented that uses a coupled shape model in conjunction with a neural network to overcome these challenges. The network provides a preliminary segmentation of the teeth region which is used to initialize the coupled shape model in terms of position and scale. Then the 28 individual teeth (excluding wisdom teeth) are segmented and labeled using gradient image features in combination with the model's statistical knowledge about their shape variation and spatial relation. The segmentation quality of the approach is assessed by comparing the generated results to manually created goldstandard segmentations of the individual teeth. Experimental results on a set of 14 test images show average precision and recall values of 0.790 and 0.827, respectively and a DICE overlap of 0.744.
Author(s)
Wirtz, Andreas  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mirashi, Sudesh Ganapati
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Wesarg, Stefan  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
Medical Image Computing and Computer Assisted Intervention, MICCAI 2018  
Conference
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018  
DOI
10.1007/978-3-030-00937-3_81
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Individual Health

  • Research Line: Computer vision (CV)

  • Research Line: Modeling (MOD)

  • dental imaging

  • statistical shape model (SSM)

  • Convolutional Neural Networks (CNN)

  • model based segmentations

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