Dental pathology detection in 3D cone-beam CT [article]

Adel Zakirov, Matvey Ezhov, Maxim Gusarev, Vladimir Alexandrovsky, Evgeny Shumilov
<span title="2018-10-24">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Cone-beam computed tomography (CBCT) is a valuable imaging method in dental diagnostics that provides information not available in traditional 2D imaging. However, interpretation of CBCT images is a time-consuming process that requires a 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 a 3D image volume,
more &raquo; ... and b) detect several common tooth conditions in each tooth. The proposed system achieves 96.3\% accuracy in tooth localization and an average of 0.94 AUROC for 6 common tooth conditions.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1810.10309v1</a> <a target="_blank" rel="external noopener" href="">fatcat:ys6n7ziggrdlhds2necfqislje</a> </span>
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