Abstract
Spinal diseases are common and difficult to cure, which causes much suffering. Accurate diagnosis and assessment of these diseases can considerably improve cure rates and the quality of life for patients. The spinal disease assessment relies primarily on accurate vertebra landmark detection, such as scoliosis assessment. However, existing approaches do not adequately exploit the relationships between vertebrae and analyze the global spine structure, meaning scarcity annotations are underutilized. In addition, the practical design of ground-truth is also deficient in model learning due to the suboptimal coordinate system. Therefore, we propose a unified end-to-end vertebra landmark detection network called Dcor-VLDet, contributing to the scoliosis assessment task. This network takes the positional information from within and between vertebrae into account. At the same time, through fusing the advantages of both Cartesian and polar coordinate systems, the symmetric mean absolute percentage error (SMAPE) value can be reduced significantly in scoliosis assessment. The experimental results demonstrate that our proposed method is superior in measuring Cobb angle and detecting landmarks on low-contrast X-ray images.
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Zhang, H., Mok, T.C.W., Chung, A.C.S. (2022). Dcor-VLDet: A Vertebra Landmark Detection Network for Scoliosis Assessment with Dual Coordinate System. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds) Machine Learning in Medical Imaging. MLMI 2022. Lecture Notes in Computer Science, vol 13583. Springer, Cham. https://doi.org/10.1007/978-3-031-21014-3_8
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DOI: https://doi.org/10.1007/978-3-031-21014-3_8
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