End-to-end dental pathology detection in 3D cone-beam computed tomography imagesDownload PDF

11 Apr 2018 (modified: 16 May 2018)MIDL 2018 Conference SubmissionReaders: Everyone
  • Abstract: Cone-beam computed tomography (CBCT) is valuable imaging method in dental diagnostics that provides information not available in traditional 2D imaging. However, interpretation of CBCT images is time-consuming process that requires physician to work with complicated software. In this work we propose an automated pipeline composed of several deep convolutional neural networks and algorithmic heuristics. Our task is two-fold: a) find locations of each present tooth inside 3D image volume, and b) detect several common tooth conditions in each tooth. Proposed system achieves 96.3% accuracy in tooth localization and average of 0.94 ROC AUC for 6 common tooth conditions.
  • Keywords: deep learning, medical image analysis, CBCT, dental X-ray, segmentation, classification
  • Author Affiliation: Diagnocat
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