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Dental pathology detection in 3D cone-beam CT
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<span title="2018-10-24">2018</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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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,
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... 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.
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