Cancellous Bone Segmentation Network in Cone Beam CT Images for Post-Orthognathic Assessment of Condylar Resorption.
Authors
Affiliations (4)
Affiliations (4)
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China.
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China.
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, 100081, China.
- School of Electronics and Information Engineering, Beijing Jiaotong University, # No.3, Nanxiaojie, Xizhimenwai, Haidian District, Beijing, 100044, China.
Abstract
Reliable cancellous bone segmentation in Cone Beam CT (CBCT) images is essential for post-orthognathic assessment of condylar resorption. However, challenges such as edge blurring and low contrast in CBCT images make effective segmentation difficult. This study aims to overcome these issues, providing a foundation for accurate bone quantification to enhance surgical planning and patient outcomes. We propose a novel approach to enhance edge-based segmentation for cancellous bone in CBCT images. By incorporating edge features from the cancellous bone region and utilizing cancellous edge localization as an auxiliary task via Dual-Branch Fusion Network (DBF-Net), our model leverages shared feature parameters across functions to improve segmentation accuracy and robustness. Our DBF-Net outperformed other models, achieving DICE coefficient of 91.48%. And the 95% Hausdorff Distance decreased to 3.88 mm, demonstrating significant improvement in cancellous bone boundary detection, which is crucial for the post-orthognathic assessment of condylar resorption. This method provides a robust solution for reliable cancellous bone segmentation in CBCT images to support the quantitative assessment of condylar resorption.