IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect Repair
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 c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/4/407 |
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| author | Xueqin Ji Wensheng Wang Xiaobiao Zhang Xinrong Chen |
| author_facet | Xueqin Ji Wensheng Wang Xiaobiao Zhang Xinrong Chen |
| author_sort | Xueqin Ji |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aab13176b3c444afacdf03a8e84f487b |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-aab13176b3c444afacdf03a8e84f487b2025-08-20T02:17:14ZengMDPI AGBioengineering2306-53542025-04-0112440710.3390/bioengineering12040407IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect RepairXueqin Ji0Wensheng Wang1Xiaobiao Zhang2Xinrong Chen3The Third School of Clinical Medicine, Ningxia Medical University, Yinchuan 750000, ChinaFudan University Academy for Engineering and Technology, Shanghai 200000, ChinaShanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200000, ChinaFudan University Academy for Engineering and Technology, Shanghai 200000, ChinaCranio-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.https://www.mdpi.com/2306-5354/12/4/407cranio-maxillofacial surgerybone defect repairdiffusion modeldeep learningmedical image segmentation3D reconstruction |
| spellingShingle | Xueqin Ji Wensheng Wang Xiaobiao Zhang Xinrong Chen IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect Repair Bioengineering cranio-maxillofacial surgery bone defect repair diffusion model deep learning medical image segmentation 3D reconstruction |
| title | IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect Repair |
| title_full | IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect Repair |
| title_fullStr | IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect Repair |
| title_full_unstemmed | IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect Repair |
| title_short | IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect Repair |
| title_sort | idnet a diffusion model enhanced framework for accurate cranio maxillofacial bone defect repair |
| topic | cranio-maxillofacial surgery bone defect repair diffusion model deep learning medical image segmentation 3D reconstruction |
| url | https://www.mdpi.com/2306-5354/12/4/407 |
| work_keys_str_mv | AT xueqinji idnetadiffusionmodelenhancedframeworkforaccuratecraniomaxillofacialbonedefectrepair AT wenshengwang idnetadiffusionmodelenhancedframeworkforaccuratecraniomaxillofacialbonedefectrepair AT xiaobiaozhang idnetadiffusionmodelenhancedframeworkforaccuratecraniomaxillofacialbonedefectrepair AT xinrongchen idnetadiffusionmodelenhancedframeworkforaccuratecraniomaxillofacialbonedefectrepair |