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.
Keywords: 3D reconstruction; bone defect repair; cranio-maxillofacial surgery; deep learning; diffusion model; medical image segmentation.