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. 2024 Nov 28;28(12):663.
doi: 10.1007/s00784-024-06061-y.

Automatic jawbone structure segmentation on dental CBCT images via deep learning

Yuan Tian  1 , Jin Hao  2 , Mingzheng Wang  3 , Zhejia Zhang  3 , Ge Wang  3 , Dazhi Kou  4 , Lichao Liu  3 , Xiaolin Liu  3 , Jie Tian  3
Affiliations

Automatic jawbone structure segmentation on dental CBCT images via deep learning

Yuan Tian et al. Clin Oral Investig. .

Abstract

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.

Keywords: Artificial intelligence; Cancellous bone segmentation; Cone beam computed tomography; Cortical bone segmentation; Deep learning; Jawbone segmentation.

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Conflict of interest statement

Declarations. Ethical approval: This study was approved by the institutional review boards of West China College of Stomatology, Sichuan University (WCHSIRB-D-2021-331). This study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Competing interests: The authors declare no competing interests.

References

    1. Merheb J, Van Assche N, Coucke W, Jacobs R, Naert I, Quirynen M (2010) Relationship between cortical bone thickness or computerized tomography-derived bone density values and implant stability. Clin Oral Implants Res 21(6):612–617. https://doi.org/10.1111/j.1600-0501.2009.01880.x - DOI - PubMed
    1. Alrbata RH, Yu W, Kyung HM (2014) Biomechanical effectiveness of cortical bone thickness on orthodontic microimplant stability: an evaluation based on the load share between cortical and cancellous bone. Am J Orthod Dentofac Orthop 146(2):175–182. https://doi.org/10.1016/j.ajodo.2014.04.018 - DOI
    1. Lee MY, Park JH, Kim SC, Kang KH, Cho JH, Cho JW, Chang NY, Chae JM (2016) Bone density effects on the success rate of orthodontic microimplants evaluated with cone-beam computed tomography. Am J Orthod Dentofac Orthop 149(2):217–224. https://doi.org/10.1016/j.ajodo.2015.07.037 - DOI
    1. Wang SH, Hsu JT, Fuh LJ, Peng SL, Huang HL, Tsai MT (2023) New classification for bone type at dental implant sites: a dental computed tomography study. BMC Oral Health 23(1):324. https://doi.org/10.1186/s12903-023-03039-2 - DOI - PubMed - PMC
    1. Kapila SD, Nervina JM (2015) CBCT in orthodontics: assessment of treatment outcomes and indications for its use. Dento Maxillo Facial Radiol 44(1):20140282. https://doi.org/10.1259/dmfr.20140282 - DOI

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