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

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Date
2017
Notes
April 2017, Melbourne Australia
Dewey
Intelligence artificielle
Sujet
Medical imaging; machine learning
DOI
http://dx.doi.org/10.1109/ISBI.2017.7950731
Book title
14th IEEE International Symposium on Biomedical Imaging (ISBI)
Author
Olivier Salvado, Gary Egan
Publisher
IEEE Signal Processing Society
Publisher city
New York
Year
2017
ISBN
978-1-5090-1172-8
Forthcoming
oui
URI
https://basepub.dauphine.fr/handle/123456789/16506
Collections
  • LAMSADE : Publications
Metadata
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Author
Evain, Timothée
Ripoche, Xavier
Atif, Jamal
Bloch, Isabelle
Type
Communication / Conférence
Item number of pages
1197-1200
Abstract (EN)
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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