Automatic segmentation of inferior alveolar canal with ambiguity classification in panoramic images using deep learning
Shuo Yang,
An Li,
Ping Li,
Zhaoqiang Yun,
Guoye Lin,
Jun Cheng,
Shulan Xu,
Bingjiang Qiu
Affiliations
Shuo Yang
Center of Oral Implantology, Stomatological Hospital, Southern Medical University, Guangzhou, China
An Li
Center of Oral Implantology, Stomatological Hospital, Southern Medical University, Guangzhou, China
Ping Li
Center of Oral Implantology, Stomatological Hospital, Southern Medical University, Guangzhou, China
Zhaoqiang Yun
Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
Guoye Lin
Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
Jun Cheng
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
Shulan Xu
Center of Oral Implantology, Stomatological Hospital, Southern Medical University, Guangzhou, China; Corresponding author. Center of Oral Implantology, Stomatological Hospital, Southern Medical University, No.366 Jiangnan S Ave, 510280, Guangzhou, China.
Bingjiang Qiu
Department of Radiology & Guangdong Cardiovascular Institute & Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China; Data Science Center in Health (DASH) & 3D Lab, University Medical Center Groningen, University of Groningen, Groningen, Netherlands; Corresponding author. Department of Radiology & Guangdong Cardiovascular Institute & Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Background: Manual segmentation of the inferior alveolar canal (IAC) in panoramic images requires considerable time and labor even for dental experts having extensive experience. The objective of this study was to evaluate the performance of automatic segmentation of IAC with ambiguity classification in panoramic images using a deep learning method. Methods: Among 1366 panoramic images, 1000 were selected as the training dataset and the remaining 336 were assigned to the testing dataset. The radiologists divided the testing dataset into four groups according to the quality of the visible segments of IAC. The segmentation time, dice similarity coefficient (DSC), precision, and recall rate were calculated to evaluate the efficiency and segmentation performance of deep learning-based automatic segmentation. Results: Automatic segmentation achieved a DSC of 85.7% (95% confidence interval [CI] 75.4%–90.3%), precision of 84.1% (95% CI 78.4%–89.3%), and recall of 87.7% (95% CI 77.7%–93.4%). Compared with manual annotation (5.9s per image), automatic segmentation significantly increased the efficiency of IAC segmentation (33 ms per image). The DSC and precision values of group 4 (most visible) were significantly better than those of group 1 (least visible). The recall values of groups 3 and 4 were significantly better than those of group 1. Conclusions: The deep learning-based method achieved high performance for IAC segmentation in panoramic images under different visibilities and was positively correlated with IAC image clarity.
