Accurate Cobb Angle Estimation via SVD-Based Curve Detection and Vertebral Wedging Quantification

作者：Shi Chang, Meng, Nan, Zhuang, Yipeng, Moxin Zhao, Jason Pui Yin Cheung, Hua Huang, Xiuyuan Chen, Cong Nie, Wenting Zhong, Jiang, Guiqiang, Yuxin Wei, Jingcui Yu, Si Chen, Xiaowen Ou, Teng Zhang · 发表于：arXiv (Cornell University) · 年份：2025 · DOI：10.48550/arxiv.2509.24898 · 研究领域：Medical Imaging and Analysis、Scoliosis diagnosis and treatment、Spine and Intervertebral Disc Pathology

Adolescent idiopathic scoliosis (AIS) is a common spinal deformity affecting approximately 2.2% of boys and 4.8% of girls worldwide. The Cobb angle serves as the gold standard for AIS severity assessment, yet traditional manual measurements suffer from significant observer variability, compromising diagnostic accuracy. Despite prior automation attempts, existing methods use simplified spinal models and predetermined curve patterns that fail to address clinical complexity. We present a novel deep learning framework for AIS assessment that simultaneously predicts both superior and inferior endplate angles with corresponding midpoint coordinates for each vertebra, preserving the anatomical reality of vertebral wedging in progressive AIS. Our approach combines an HRNet backbone with Swin-Transformer modules and biomechanically informed constraints for enhanced feature extraction. We employ Singular Value Decomposition (SVD) to analyze angle predictions directly from vertebral morphology, enabling flexible detection of diverse scoliosis patterns without predefined curve assumptions. Using 630 full-spine anteroposterior radiographs from patients aged 10-18 years with rigorous dual-rater annotation, our method achieved 83.45% diagnostic accuracy and 2.55° mean absolute error. The framework demonstrates exceptional generalization capability on out-of-distribution cases. Additionally, we introduce the Vertebral Wedging Index (VWI), a novel metric quantifying vertebral deformation. Lon...