Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgery
ObjectivesAccurate segmentation of craniomaxillofacial (CMF) structures and individual teeth is essential for advancing computer-assisted CMF surgery. This study developed CMF-ELSeg, a novel fully automatic multi-structure segmentation model based on deep ensemble learning.MethodsA total of 143 CMF...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1580502/full |
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| author | Jiahao Bao Zongcai Tan Yifeng Sun Xinyu Xu Huazhen Liu Weiyi Cui Yang Yang Mengjia Cheng Yiming Wang Congshuang Ku Yuen Ka Ho Jiayi Zhu Linfeng Fan Dahong Qian Shunyao Shen Yaofeng Wen Hongbo Yu |
| author_facet | Jiahao Bao Zongcai Tan Yifeng Sun Xinyu Xu Huazhen Liu Weiyi Cui Yang Yang Mengjia Cheng Yiming Wang Congshuang Ku Yuen Ka Ho Jiayi Zhu Linfeng Fan Dahong Qian Shunyao Shen Yaofeng Wen Hongbo Yu |
| author_sort | Jiahao Bao |
| collection | DOAJ |
| description | ObjectivesAccurate segmentation of craniomaxillofacial (CMF) structures and individual teeth is essential for advancing computer-assisted CMF surgery. This study developed CMF-ELSeg, a novel fully automatic multi-structure segmentation model based on deep ensemble learning.MethodsA total of 143 CMF computed tomography (CT) scans were retrospectively collected and manually annotated by experts for model training and validation. Three 3D U-Net–based deep learning models (V-Net, nnU-Net, and 3D UX-Net) were benchmarked. CMF-ELSeg employed a coarse-to-fine cascaded architecture and an ensemble approach to integrate the strengths of these models. Segmentation performance was evaluated using Dice score and Intersection over Union (IoU) by comparing model predictions to ground truth annotations. Clinical feasibility was assessed through qualitative and quantitative analyses.ResultsIn coarse segmentation of the upper skull, mandible, cervical vertebra, and pharyngeal cavity, 3D UX-Net and nnU-Net achieved Dice scores above 0.96 and IoU above 0.93. For fine segmentation and classification of individual teeth, the cascaded 3D UX-Net performed best. CMF-ELSeg improved Dice scores by 3%–5% over individual models for facial soft tissue, upper skull, mandible, cervical vertebra, and pharyngeal cavity segmentation, and maintained high accuracy Dice > 0.94 for most teeth. Clinical evaluation confirmed that CMF-ELSeg performed reliably in patients with skeletal malocclusion, fractures, and fibrous dysplasia.ConclusionCMF-ELSeg provides high-precision segmentation of CMF structures and teeth by leveraging multiple models, serving as a practical tool for clinical applications and enhancing patient-specific treatment planning in CMF surgery. |
| format | Article |
| id | doaj-art-727d087834794b028c8309ca27b4272d |
| institution | DOAJ |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-727d087834794b028c8309ca27b4272d2025-08-20T02:57:25ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-05-011310.3389/fbioe.2025.15805021580502Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgeryJiahao Bao0Zongcai Tan1Yifeng Sun2Xinyu Xu3Huazhen Liu4Weiyi Cui5Yang Yang6Mengjia Cheng7Yiming Wang8Congshuang Ku9Yuen Ka Ho10Jiayi Zhu11Linfeng Fan12Dahong Qian13Shunyao Shen14Yaofeng Wen15Hongbo Yu16Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Research Institute of Stomatology, Shanghai Key Laboratory of Stomatology, Shanghai, ChinaHamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London, United KingdomSchool of Mechanical Engineering, Shanghai Dianji University, Shanghai, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Research Institute of Stomatology, Shanghai Key Laboratory of Stomatology, Shanghai, ChinaShanghai Lanhui Medical Technology Co., Ltd., Shanghai, ChinaFaculty of Dentistry, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Research Institute of Stomatology, Shanghai Key Laboratory of Stomatology, Shanghai, ChinaDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Research Institute of Stomatology, Shanghai Key Laboratory of Stomatology, Shanghai, ChinaDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Research Institute of Stomatology, Shanghai Key Laboratory of Stomatology, Shanghai, ChinaDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Research Institute of Stomatology, Shanghai Key Laboratory of Stomatology, Shanghai, ChinaDepartment of Radiology, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Research Institute of Stomatology, Shanghai Key Laboratory of Stomatology, Shanghai, ChinaSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Research Institute of Stomatology, Shanghai Key Laboratory of Stomatology, Shanghai, ChinaObjectivesAccurate segmentation of craniomaxillofacial (CMF) structures and individual teeth is essential for advancing computer-assisted CMF surgery. This study developed CMF-ELSeg, a novel fully automatic multi-structure segmentation model based on deep ensemble learning.MethodsA total of 143 CMF computed tomography (CT) scans were retrospectively collected and manually annotated by experts for model training and validation. Three 3D U-Net–based deep learning models (V-Net, nnU-Net, and 3D UX-Net) were benchmarked. CMF-ELSeg employed a coarse-to-fine cascaded architecture and an ensemble approach to integrate the strengths of these models. Segmentation performance was evaluated using Dice score and Intersection over Union (IoU) by comparing model predictions to ground truth annotations. Clinical feasibility was assessed through qualitative and quantitative analyses.ResultsIn coarse segmentation of the upper skull, mandible, cervical vertebra, and pharyngeal cavity, 3D UX-Net and nnU-Net achieved Dice scores above 0.96 and IoU above 0.93. For fine segmentation and classification of individual teeth, the cascaded 3D UX-Net performed best. CMF-ELSeg improved Dice scores by 3%–5% over individual models for facial soft tissue, upper skull, mandible, cervical vertebra, and pharyngeal cavity segmentation, and maintained high accuracy Dice > 0.94 for most teeth. Clinical evaluation confirmed that CMF-ELSeg performed reliably in patients with skeletal malocclusion, fractures, and fibrous dysplasia.ConclusionCMF-ELSeg provides high-precision segmentation of CMF structures and teeth by leveraging multiple models, serving as a practical tool for clinical applications and enhancing patient-specific treatment planning in CMF surgery.https://www.frontiersin.org/articles/10.3389/fbioe.2025.1580502/fulldeep learningcraniomaxillofacial surgeryvirtual surgical planningcomputed tomographysegmentation |
| spellingShingle | Jiahao Bao Zongcai Tan Yifeng Sun Xinyu Xu Huazhen Liu Weiyi Cui Yang Yang Mengjia Cheng Yiming Wang Congshuang Ku Yuen Ka Ho Jiayi Zhu Linfeng Fan Dahong Qian Shunyao Shen Yaofeng Wen Hongbo Yu Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgery Frontiers in Bioengineering and Biotechnology deep learning craniomaxillofacial surgery virtual surgical planning computed tomography segmentation |
| title | Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgery |
| title_full | Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgery |
| title_fullStr | Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgery |
| title_full_unstemmed | Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgery |
| title_short | Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgery |
| title_sort | deep ensemble learning driven fully automated multi structure segmentation for precision craniomaxillofacial surgery |
| topic | deep learning craniomaxillofacial surgery virtual surgical planning computed tomography segmentation |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1580502/full |
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