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

Evain, Timothée; Ripoche, Xavier; Atif, Jamal; Bloch, Isabelle (2017), Semi-automatic teeth segmentation in cone-beam computed tomography by graph-cut with statistical shape priors, in Olivier Salvado, Gary Egan, 14th IEEE International Symposium on Biomedical Imaging (ISBI), IEEE Signal Processing Society : New York, p. 1197-1200. 10.1109/ISBI.2017.7950731

Type
Communication / Conférence
Date
2017
Book title
14th IEEE International Symposium on Biomedical Imaging (ISBI)
Book author
Olivier Salvado, Gary Egan
Publisher
IEEE Signal Processing Society
Published in
New York
ISBN
978-1-5090-1172-8
Pages
1197-1200
Publication identifier
Metadata
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Author(s)
Evain, Timothée
Ripoche, Xavier
Atif, Jamal
Bloch, Isabelle cc
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.
Subjects / Keywords
Medical imaging; machine learning