| This work presents an automated dual-stage deep learning framework for vertebral segmentation and scoliosis assessment from CT images, combining YOLOv8 object detection with Attention U-Net segmentation. YOLOv8 first localizes individual vertebrae by generating precise bounding boxes that isolate regions of interest, which are then processed by the Attention U-Net, whose attention gates suppress irrelevant background while enhancing salient vertebral features for accurate boundary delineation. Trained on a manually annotated CT dataset, the framework achieves near-perfect detection precision and recall, a Dice coefficient of 0.97, and an IoU of 0.94. The system further extracts vertebral centroids to construct a linear spinal representation, enabling quantitative curvature analysis and automated scoliosis severity classification, offering a robust and fully automated tool for clinical monitoring and treatment planning. |
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