Development of a Novel Machine Learning Model to Automate Vertebral Column Segmentation Utilizing Biplanar Full-body Imaging.
Journal:
The spine journal : official journal of the North American Spine Society
Published Date:
May 3, 2025
Abstract
BACKGROUND CONTEXT: Degenerative scoliosis (DS) is a common spinal disorder among adults, characterized by lateral curvature of the spine. Recent advancements in biplanar full-body imaging, a low-dose and weight-bearing X-ray modality, facilitate safer and longitudinal imaging of DS patients. Quantifying spinal curvature serves as a valuable metric for assessing DS severity and informing surgical planning. However, manual annotation of vertebral structures in radiographic images is labor-intensive, necessitating specialized expertise and resulting in significant inter- and intra-observer variability. Advances in deep learning computer models, particularly with convolutional neural networks (CNNs) employing UNET architecture, offer robust solutions for image segmentation tasks. These deep learning approaches have the potential to standardize and expedite the analysis of spinal alignment alterations throughout disease progression.
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