Automatic mandibular canal detection using a deep convolutional neural network.
Gloria Hyunjung Kwak, Eun-Jung Kwak, Jae Min Song, Hae Ryoun Park, Yun-Hoa Jung, Bong-Hae Cho, Pan Hui, Jae Joon Hwang
Author Information
Gloria Hyunjung Kwak: Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Pokfulam, Hong Kong. ORCID
Eun-Jung Kwak: National Dental Care Center for Persons with Special Needs, Seoul National University Dental Hospital, Seoul, Korea. ORCID
Jae Min Song: Department of oral and maxillofacial surgery, school of dentistry, Pusan National University, Pusan, Korea. ORCID
Hae Ryoun Park: Department of Oral Pathology & BK21 PLUS Project, School of Dentistry, Pusan National University, Yangsan, Korea.
Yun-Hoa Jung: Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Dental and Life Science Institute, Yangsan, Korea.
Bong-Hae Cho: Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Dental and Life Science Institute, Yangsan, Korea.
Pan Hui: Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Pokfulam, Hong Kong.
Jae Joon Hwang: Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Dental and Life Science Institute, Yangsan, Korea. softdent@pusan.ac.kr. ORCID
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.
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