Automatic mandibular canal detection using a deep convolutional neural network

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Kwak , G H , Kwak , E-J , Song , J M , Park , H R , Jung , Y-H , Cho , B-H , Hui , P & Hwang , J J 2020 , ' Automatic mandibular canal detection using a deep convolutional neural network ' , Scientific Reports , vol. 10 , no. 1 , 5711 .

Titel: Automatic mandibular canal detection using a deep convolutional neural network
Författare: Kwak, Gloria Hyunjung; Kwak, Eun-Jung; Song, Jae Min; Park, Hae Ryoun; Jung, Yun-Hoa; Cho, Bong-Hae; Hui, Pan; Hwang, Jae Joon
Medarbetare: University of Helsinki, Department of Computer Science
Datum: 2020-03-31
Språk: eng
Sidantal: 8
Tillhör serie: Scientific Reports
ISSN: 2045-2322
Permanenta länken (URI):
Abstrakt: The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients’ discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.
Subject: 3111 Biomedicine
113 Computer and information sciences

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