Affiliation:
1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Abstract
Precise surveillance and assessment of spinal disorders are important for improving health care and patient survival rates. The assessment of spinal disorders, such as scoliosis assessment, depends heavily on precise vertebra landmark localization. However, existing methods usually search for only a handful of keypoints in a high-resolution image. In this paper, we propose the S2D-VLI VLDet network, a unified end-to-end vertebra landmark detection network for the assessment of scoliosis. The proposed network considers the spatially relevant information both from inside and between vertebrae. The new vertebral line interpolation method converts the training labels from sparse to dense, which can improve the network learning process and method performance. In addition, through the combined use of the Cartesian and polar coordinate systems in our method, the symmetric mean absolute percentage error (SMAPE) in scoliosis assessment can be reduced substantially. Specifically, as shown in the experiments, the SMAPE value decreases from 9.82 to 8.28. The experimental results indicate that our proposed approach is beneficial for estimating the Cobb angle and identifying landmarks in X-ray scans with low contrast.
Funder
Hong Kong Research Grants Council
Reference66 articles.
1. The impact of positive sagittal balance in adult spinal deformity;Glassman;Spine,2005
2. Outline for the study of scoliosis;Cobb;Instr. Course Lect. AAOS,1948
3. Inter-and intraobserver reliability assessment of the Cobb angle: Manual versus digital measurement tools;Gstoettner;Eur. Spine J.,2007
4. Zhang, H., Mok, T.C., and Chung, A.C. (2022, January 18–22). Dcor-VLDet: A Vertebra Landmark Detection Network for Scoliosis Assessment with Dual Coordinate System. Proceedings of the International Workshop on Machine Learning in Medical Imaging, Singapore.
5. Hsu, C.F., Lin, C.C., Hung, T.Y., Lei, C.L., and Chen, K.T. (2020). A Detailed Look At CNN-based Approaches In Facial Landmark Detection. arXiv.