Geometry-Aware 1D Residual CNN-BiLSTM Network for Automated Vertebral Localization and Scoliosis Classification

Abstract

Accurate localization of the upper end vertebra (UEV), lower end vertebra (LEV), and apex vertebra (AV) is essential for automated scoliosis assessment and Cobb angle measurement. This study proposes a geometry-aware, multi-task sequence learning framework for vertebral landmark localization and scoliosis type classification. The framework integrates YOLOv8-OBB-based feature extraction with a 1D Residual CNN-BiLSTM architecture and a displacement-guided regularization term, termed Geometric Loss (GeoLoss). Images and reference annotations were obtained from the open-source Spinal-AI2024 dataset, and a development set of 550 radiographs was used for model development and internal evaluation. Vertebral detections were transformed into 12-dimensional geometric sequences encoding orientation, displacement, shape, confidence, and curvature. Robustness was assessed using four train–validation–test partitioning strategies with repeated experiments and five-fold cross-validation, with the 75/15/10 partition showing the strongest mean internal performance. On a disjoint held-out set of 58 unseen images from the same dataset, the model achieved a Type Accuracy, EV Accuracy (±1), and EV Accuracy (±2) of 96.55%, 86.63%, and 94.19%, respectively, with an EV mean absolute error of 0.559 vertebral levels. These findings support geometry-aware sequence modeling as an effective approach for automated vertebral landmark localization and scoliosis assessment.