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Semi-automatic teeth segmentation in cone-beam computed tomography by graph-cut with statistical shape prior

Timothée Evain 1, 2 Xavier Ripoche J. Atif Isabelle Bloch 1, 2
1 IMAGES - Image, Modélisation, Analyse, GEométrie, Synthèse
LTCI - Laboratoire Traitement et Communication de l'Information
2 IDS - Département Images, Données, Signal
Abstract : We propose a new semi-automatic framework for tooth segmentation in Cone-Beam Computed Tomography (CBCT) combining shape priors based on a statistical shape model and graph cut optimization. Poor image quality and similarity between tooth and cortical bone intensities are overcome by strong constraints on the shape and on the targeted area. The segmentation quality was assessed on 64 tooth images for which a reference segmentation was available, with an overall Dice coefficient above 0.95 and a global consistency error less than 0.005.
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Conference papers
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https://hal.telecom-paris.fr/hal-02288484
Contributor : Telecomparis Hal <>
Submitted on : Saturday, September 14, 2019 - 6:52:37 PM
Last modification on : Wednesday, September 30, 2020 - 8:54:14 AM

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  • HAL Id : hal-02288484, version 1

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LTCI | PARISTECH | INSTITUT-TELECOM | IDS | IMAGES

Citation

Timothée Evain, Xavier Ripoche, J. Atif, Isabelle Bloch. Semi-automatic teeth segmentation in cone-beam computed tomography by graph-cut with statistical shape prior. IEEE International Symposium on Biomedical Imaging, 2017, Melbourne, Australia. pp.1197-1200. ⟨hal-02288484⟩

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