Automatic mandibular canal detection using a deep convolutional neural network

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dc.contributor University of Helsinki, Department of Computer Science en Kwak, Gloria Hyunjung Kwak, Eun-Jung Song, Jae Min Park, Hae Ryoun Jung, Yun-Hoa Cho, Bong-Hae Hui, Pan Hwang, Jae Joon 2020-08-20T12:41:01Z 2020-08-20T12:41:01Z 2020-03-31
dc.identifier.citation 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 . en
dc.identifier.issn 2045-2322
dc.identifier.other PURE: 135415473
dc.identifier.other PURE UUID: eef7728a-0e04-4549-9c13-04dc51229e26
dc.identifier.other RIS: urn:F448A7D9212490A692C1AA46FEA96EBD
dc.identifier.other RIS: Kwak2020
dc.identifier.other WOS: 000563291100008
dc.description.abstract 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. en
dc.format.extent 8
dc.language.iso eng
dc.relation.ispartof Scientific Reports
dc.rights en
dc.subject 3111 Biomedicine en
dc.subject 113 Computer and information sciences en
dc.subject SEGMENTATION en
dc.subject POSITION en
dc.subject CT en
dc.title Automatic mandibular canal detection using a deep convolutional neural network en
dc.type Article
dc.description.version Peer reviewed
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion

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