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

: Wirtz, Andreas; Mirashi, Sudesh Ganapati; Wesarg, Stefan


Frangi, Alejandro F. (Ed.):
Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 : 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 11073)
ISBN: 978-3-030-00936-6 (Print)
ISBN: 978-3-030-00937-3 (Online)
ISBN: 978-3-030-00938-0
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) <21, 2018, Granada>
Conference Paper
Fraunhofer IGD ()
Guiding Theme: Individual Health; Research Area: Computer vision (CV); Research Area: Modeling (MOD); dental imaging; statistical shape model (SSM); Convolutional Neural Networks (CNN); model based segmentations

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.