Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application.

Autor: Wu Q; Department of Maxillofacial Surgery and Oral Implantology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang Province, China., Feng B; Department of Oral and Maxillofacial Radiology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China., Li W; School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang Province, China., Zhang W; Department of Oral and Maxillofacial Radiology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China., Wang J; Department of Oral and Maxillofacial Radiology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China., Wang X; Department of Oral and Maxillofacial Radiology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China., Dai J; Department of Oral and Maxillofacial Radiology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China., Jin C; Shanghai Pinghe Bilingual School, Shanghai, 201206, China., Wu F; School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang Province, China. fuliwu@zjut.edu.cn., Yu M; Department of Maxillofacial Surgery and Oral Implantology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang Province, China. yumengfei@zju.edu.cn., Zhu F; Department of Maxillofacial Surgery and Oral Implantology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang Province, China. zfd@zju.edu.cn.
Jazyk: angličtina
Zdroj: Oral radiology [Oral Radiol] 2024 Nov 09. Date of Electronic Publication: 2024 Nov 09.
DOI: 10.1007/s11282-024-00780-4
Abstrakt: Objectives: To develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application.
Methods: 825 condyles of 490 CBCT images from 3 centers of Stomatology hospital affliated to Zhejiang University School of Medicine were collected. A deep learning model was developed for simultaneous segmentation of cortex and marrow in mandibular condyle. It included a region of interest extraction network and a segmentation network based on 3D U-net, with modifications made to improve the segmentation boundaries. To evaluate its clinical potential, the model's segmentation efficiency and accuracy were compared with those of both junior and senior oral and maxillofacial radiologists. Additionally, the model's ability to assist junior radiologists in diagnosis through visualization and quantitative analysis of the generated 3D model was also assessed.
Results: The Dice similarity coefficient of the deep learning model was 0.901 (cortex), 0.969 (marrow), and 0.982 (entire condyle). Hausdorff distance was 0.755 mm (cortex), 0.826 mm (marrow), and 0.760 mm (entire condyle). The model outperformed radiologists across all segmentation metrics, completing the task in merely 15.06 s. With the assistance of visualization and quantitative analysis generated from the model's segmentation, the diagnostic accuracy of junior radiologists significantly improved.
Conclusions: The proposed deep learning-based model achieved accurate and efficient segmentation for mandibular condylar cortex and marrow. It possessed capability to generate precise 3D models, facilitating visual quantitative measurement and aiding in the diagnosis of condylar bony changes. This model holds potential for clinical applications in orthognathic surgery, orthodontic treatment, and other TMJ-related interventions.
(© 2024. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)
Databáze: MEDLINE
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