Congress 1186
  • N. Vila-Blanco, T. Cootes, C. Lindner, I. Tomás, M.J. Carreira
  • 21st International Conference on Medical Image Computing and Computer Assisted Intervention (6th Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging). Granada, España. 2018

Fully automatic teeth segmentation in adult OPG images

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.
Keywords: teeth segmentation, panoramic dental images, random forest regression-voting, machine learning
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