Dual-stage anatomical perception and reasoning framework for automated cobb angle estimation

Accurate and automated Cobb angle measurement is essential for the diagnosis and management of Adolescent Idiopathic Scoliosis (AIS). However, existing deep learning approaches often ignore the intrinsic anatomical dependencies of the spine, which results in physiologically implausible estimations. We propose a two-stage anatomical perception and reasoning framework based on keypoint detection for Cobb angle measurement. The anatomical perception stage is built upon a Local-to-Global Aggregation Backbone (LGAB), which combines convolutional neural networks and Transformers to jointly capture local anatomical details and global spinal context. The resulting feature representations are further refined by a Semantic Guidance Module (SGM) to facilitate cross-scale feature fusion and improve vertebral keypoint localization. In the second stage anatomical reasoning, an Anatomical Reasoning Network (ARN) models all detected vertebral center keypoints as a structured graph. By embedding anatomical priors and applying dynamic relational reasoning, the ARN refines the spatial configuration of keypoints, which ensures anatomical coherence and corrects subtle localization errors. The optimized keypoints are then used to calculate the Cobb angle. The proposed framework achieves a Symmetric Mean Absolute Percentage Error (SMAPE) of 6.61%, a Cobb Mean Absolute Error (CMAE) of 2.67°, and an Error of Center ([Formula: see text]) of 24.58 pixels on the public AASCE 2019 Challenge dataset. Furthermore, the framework achieves a comparable CMAE of 2.11° on an independent clinical dataset. These results demonstrate that the proposed method provides a reliable foundation for precise and automated Cobb angle measurement.