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Published on: February 23, 2024
Cancellous bone segmentation network in cone beam CT images for post-orthognathic assessment of condylar resorption
Yuxuan Yang1, Chen Zhong1, Ruohan Ma1
1School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Objectives:
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.
Methods:
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.
Results:
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.
Conclusions:
This method provides a robust solution for reliable cancellous bone segmentation in CBCT images to support the quantitative assessment of condylar resorption.
