Deep Ensemble Learning-Driven Fully Automated Multi-Structure Segmentation for Precision Craniomaxillofacial Surgery

Jiahao Bao¹Zongcai Tan²Yifeng Sun³Xinyu Xu⁴Huazhen Liu⁴Weiyi Cui¹Yang Yang⁵Mengjia Cheng¹Yiming Wang¹Congshuang Ku¹Yuen Ka Ho¹Jiayi Zhu¹Linfeng Fan¹Dahong Qian⁴Shunyao Shen¹Yaofeng Wen⁴Hongbo Yu^1*

• ¹School of Medicine, Shanghai Jiao Tong University, Shanghai, China
• ²Imperial College London, London, England, United Kingdom
• ³Shanghai Dianji University, Shanghai, Shanghai Municipality, China
• ⁴Shanghai Jiao Tong University, Shanghai, Shanghai Municipality, China
• ⁵Shanghai Lanhui Medical Technology Co., Ltd, Shanghai, China

Objectives: Accurate 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. Methods: A 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. Results: In 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. Conclusions: CMF-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.