A Dual Coordinate System Vertebra Landmark Detection Network with Sparse-to-Dense Vertebral Line Interpolation

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...

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Bibliographic Details
Published in:Bioengineering (Basel) 2024-01, Vol.11 (1), p.101
Main Authors: Zhang, Han, Chung, Albert C S
Format: Article
Language:English
Subjects:
Automation
Care and treatment
Classification
computer-aided diagnosis
convolutional neural network
Diagnosis
Disorders
Image resolution
Interpolation
Localization
Medical examination
Medical research
Polar coordinates
Scoliosis
scoliosis assessment
Spinal diseases
Spine
vertebra landmark detection
Vertebrae
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Summary: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.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering11010101