Automatic jawbone structure segmentation on dental CBCT images via deep learning.

Objectives: This study developed and evaluated a two-stage deep learning-based system for automatic segmentation of mandibular cortical bone, mandibular cancellous bone, maxillary cortical bone and maxillary cancellous bone on cone beam computed tomography (CBCT) images.

Materials and methods: A dataset containing 155 CBCT scans acquired with different parameters was obtained. A two-stage deep learning-based system was developed for automatically segmenting jawbone structures. The Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) were used to assess the segmentation performance of the system by comparing the automatic segmentation results with the ground truth. The impact of dental and quality abnormalities on segmentation performance was analysed, and a comparison of automatic segmentation (AS) with manually refined segmentation (MRS) was reported.

Results: The system achieved promising segmentation performance, with average DSC values of 93.69%, 96.83%, 86.14% and 95.57% and average ASSD values of 0.13 mm, 0.16 mm, 0.29 mm and 0.41 mm for the mandibular cortical bone, mandibular cancellous bone, maxillary cortical bone and maxillary cancellous bone, respectively. Quality abnormalities had a negative impact on segmentation performance. The performance metrics (DSCs > 98.8% and ASSDs < 0.1 mm) indicated high overlap between the AS and MRS.

Conclusion: The proposed system offers an accurate and time-efficient method for segmenting jawbone structures on CBCT images.

Clinical relevance: Automatically segmenting jawbone structures is essential in most digital dental workflows. The proposed system has considerable potential for application in digital clinical workflows to assist dentists in making more accurate diagnoses and developing patient-specific treatment plans.