TY - GEN
T1 - Semi-automatic teeth segmentation in Cone-Beam Computed Tomography by graph-cut with statistical shape priors
AU - Evain, Timothee
AU - Ripoche, Xavier
AU - Atif, Jamal
AU - Bloch, Isabelle
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/15
Y1 - 2017/6/15
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85023193126
U2 - 10.1109/ISBI.2017.7950731
DO - 10.1109/ISBI.2017.7950731
M3 - Conference contribution
AN - SCOPUS:85023193126
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1197
EP - 1200
BT - 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PB - IEEE Computer Society
T2 - 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Y2 - 18 April 2017 through 21 April 2017
ER -