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
TypeCommunication / Conférence
Book title14th IEEE International Symposium on Biomedical Imaging (ISBI)
Book authorOlivier Salvado, Gary Egan
MetadataShow full item record
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 / KeywordsMedical imaging; machine learning
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Aiguier, Marc; Atif, Jamal; Bloch, Isabelle; Pino Pérez, Ramón (2018) Article accepté pour publication ou publié