Abstract
Purpose
For extremely close bones, their boundaries are weak and diffused due to strong interaction between adjacent surfaces. These factors prevent the accurate segmentation of bone structure. To alleviate these difficulties, we propose an automatic method for accurate bone segmentation. The method is based on a consideration of the 3D surface normal direction, which is used to detect the bone boundary in 3D CT images.
Methods
Our segmentation method is divided into three main stages. Firstly, we consider a surface tracing corrector combined with Gaussian standard deviation \(\sigma \) to improve the estimation of normal direction. Secondly, we determine an optimal value of \(\sigma \) for each surface point during this normal direction correction. Thirdly, we construct the 1D signal and refining the rough boundary along the corrected normal direction. The value of \(\sigma \) is used in the first directional derivative of the Gaussian to refine the location of the edge point along accurate normal direction. Because the normal direction is corrected and the value of \(\sigma \) is optimized, our method is robust to noise images and narrow joint space caused by joint degeneration.
Results
We applied our method to 15 wrists and 50 hip joints for evaluation. In the wrist segmentation, Dice overlap coefficient (DOC) of \(97.62 \pm 0.57\)% was obtained by our method. In the hip segmentation, fivefold cross-validations were performed for two state-of-the-art methods. Forty hip joints were used for training in two state-of-the-art methods, 10 hip joints were used for testing and performing comparisons. The DOCs of \(97.34 \pm 0.56\%\), \(98.06\pm 0.58\)%, and \(97.73 \pm 0.47\)% were achieved by our method for the pelvis, the left femoral head and the right femoral head, respectively.
Conclusion
Our method was shown to improve segmentation accuracy for several specific challenging cases. The results demonstrate that our approach achieved a superior accuracy over two state-of-the-art methods.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their valuable comments and helpful suggestions that greatly improved the papers quality. This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61571158, 61741106 and 61702135.
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Guo, H., Song, S., Wang, J. et al. 3D surface voxel tracing corrector for accurate bone segmentation. Int J CARS 13, 1549–1563 (2018). https://doi.org/10.1007/s11548-018-1804-9
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DOI: https://doi.org/10.1007/s11548-018-1804-9