ORIGINAL RESEARCH article

Front. Bioeng. Biotechnol.

Sec. Biomaterials

Volume 13 - 2025 | doi: 10.3389/fbioe.2025.1580502

This article is part of the Research TopicAdvanced Technologies for Oral and Craniomaxillofacial TherapyView all 11 articles

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

Provisionally accepted
Jiahao  BaoJiahao Bao1Zongcai  TanZongcai Tan2Yifeng  SunYifeng Sun3Xinyu  XuXinyu Xu4Huazhen  LiuHuazhen Liu4Weiyi  CuiWeiyi Cui1Yang  YangYang Yang5Mengjia  ChengMengjia Cheng1Yiming  WangYiming Wang1Congshuang  KuCongshuang Ku1Yuen  Ka HoYuen Ka Ho1Jiayi  ZhuJiayi Zhu1Linfeng  FanLinfeng Fan1Dahong  QianDahong Qian4Shunyao  ShenShunyao Shen1Yaofeng  WenYaofeng Wen4Hongbo  YuHongbo Yu1*
  • 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

The final, formatted version of the article will be published soon.

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.

Keywords: deep learning, Craniomaxillofacial surgery, virtual surgical planning, computed tomography, segmentation

Received: 20 Feb 2025; Accepted: 25 Apr 2025.

Copyright: © 2025 Bao, Tan, Sun, Xu, Liu, Cui, Yang, Cheng, Wang, Ku, Ho, Zhu, Fan, Qian, Shen, Wen and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Hongbo Yu, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

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