@inproceedings{nvilablanco‚2018fullyautomatic, title = {Fully automatic teeth segmentation in adult {OPG} images}, booktitle = {21st International Conference on Medical Image Computing and Computer Assisted Intervention (6th Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging)}, year = {2018}, abstract = {This work addresses the problem of teeth segmentation in panoramic dental images‚ specifically the detection of the adult-stage mandibular teeth. Random Forest Regression-Voting Constrained Local Models (RFRV-CLM) were used to train individual teeth shape models. In order to fully automatically initialise the teeth shape search process‚ RFRV-CLM model was trained from a set of mandible and teeth key landmark points‚ and the predicted keypoints were used to estimate the initial pose of each tooth shape. Furthermore‚ a method to detect present/missing teeth has been proposed‚ based on the quality of each tooth shape segmentation. The results of this two-step approach‚ evaluated using a set of 346 annotated images‚ show good performance in present/missing teeth detection and state-of-the-art accuracy for both correct teeth localisation and shape segmentation‚ with an average median point-to-curve error of 0.2mm.}, doi = {10.1007/978-3-030-11166-3_2}, url = {http://dx.doi.org/10.1007/978-3-030-11166-3_2}, author = {N. Vila-Blanco‚ T. Cootes‚ C. Lindner‚ I. Tom\'{a}s‚ M.J. Carreira} }