Ji Xueqin, Wang Wensheng, Zhang Xiaobiao, Chen Xinrong
The Third School of Clinical Medicine, Ningxia Medical University, Yinchuan 750000, China.
Department of Ultrasound, Peking University First Hospital Ningxia Women and Children's Hospital, Yinchuan 70000, China.
Bioengineering (Basel). 2025 Apr 11;12(4):407. doi: 10.3390/bioengineering12040407.
Cranio-maxillofacial bone defect repair poses significant challenges in oral and maxillofacial surgery due to the complex anatomy of the region and its substantial impact on patients' physiological function, aesthetic appearance, and quality of life. Inaccurate reconstruction can result in serious complications, including functional impairment and psychological trauma. Traditional methods have notable limitations for complex defects, underscoring the need for advanced computational approaches to achieve high-precision personalized reconstruction. This study presents the Internal Diffusion Network (IDNet), a novel framework that integrates a diffusion model into a standard U-shaped network to extract valuable information from input data and produce high-resolution representations for 3D medical segmentation. A Step-Uncertainty Fusion module was designed to enhance prediction robustness by combining diffusion model outputs at each inference step. The model was evaluated on a dataset consisting of 125 normal human skull 3D reconstructions and 2625 simulated cranio-maxillofacial bone defects. Quantitative evaluation revealed that IDNet outperformed mainstream methods, including UNETR and 3D U-Net, across key metrics: Dice Similarity Coefficient (DSC), True Positive Rate (RECALL), and 95th percentile Hausdorff Distance (HD95). The approach achieved an average DSC of 0.8140, RECALL of 0.8554, and HD95 of 4.35 mm across seven defect types, substantially surpassing comparison methods. This study demonstrates the significant performance advantages of diffusion model-based approaches in cranio-maxillofacial bone defect repair, with potential implications for increasing repair surgery success rates and patient satisfaction in clinical applications.
由于颅颌面区域解剖结构复杂，且对患者生理功能、美学外观和生活质量有重大影响，颅颌面骨缺损修复在口腔颌面外科手术中面临重大挑战。重建不准确可能导致严重并发症，包括功能障碍和心理创伤。传统方法对于复杂缺损存在明显局限性，这凸显了采用先进计算方法实现高精度个性化重建的必要性。本研究提出了内部扩散网络（IDNet），这是一种新颖的框架，它将扩散模型集成到标准的U形网络中，以从输入数据中提取有价值的信息，并为3D医学分割生成高分辨率表示。设计了一个步长不确定性融合模块，通过在每个推理步骤中组合扩散模型输出，增强预测的稳健性。该模型在一个由125个正常人类颅骨3D重建和2625个模拟颅颌面骨缺损组成的数据集上进行了评估。定量评估表明，在关键指标：骰子相似系数（DSC）、真阳性率（召回率）和第95百分位数豪斯多夫距离（HD95）方面，IDNet优于包括UNETR和3D U-Net在内的主流方法。该方法在七种缺损类型上实现了平均DSC为0.8140、召回率为0.8554、HD95为4.35毫米，大大超过了比较方法。本研究证明了基于扩散模型的方法在颅颌面骨缺损修复中的显著性能优势，对提高临床应用中的修复手术成功率和患者满意度具有潜在意义。
