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Using deep convolutional neural networks for three-dimensional cephalometric analysis

[Stomatology]
Alexandrovich Muraev Alexandr; Nikolay Yurievich Oborotistov; Mark Evgenievich Mokrenko; Tatyana Vyacheslavovna Shiryaeva; Olga Aleksandrovna Aleshina; Mikhail Vladimirovich Ershov; Petr Nikolaevich Emel’yanov; Luisa Robertovna Agarleva; Alexander Dolgalev; Maryanna Yevgeniyevna Zorych;

The study included the development of a new convolutional neural network (CNN) model for recognizing and fitting cephalometric points on cone-beam computed tomography (CBCT) slices for further 3D cephalometric analysis and evaluation of its accuracy. We used DICOM files for 192 cone beam tomograms in the study. Each set of files was imported into ViSurgery software (Skolkovo, Russia). Next, three-dimensional models of the patient’s soft tissues, bones, and teeth were generated, and 26 points were placed on the facial surface (soft tissue points), 38 on the skull surface (bone points), and ten dental cephalometric points per model. At the same time, the position of the points was corrected on CT plane slices in 3 planes. This study demonstrated the high efficiency of the image segmentation approach for training CNN to identify cephalometric points on CBCT. The proposed method, integrated into specialized software, has a high potential for reducing the labor intensity of the workflow.

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Keywords: cone-beam computed tomography, cephalometric point landmarking, three-dimensional cephalometrics, convolutional neural networks, automatic identification, computer-assisted diagnostics


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