DGPDT: Detection-Guided and Prompt-Driven Transformers for Automated and Generalizable Cobb Angle Estimation

Abstract

Accurate Cobb angle measurement is essential for scoliosis assessment but remains labor-intensive and observer-dependent. We introduce DGPDT (Detection-Guided Prompt-driven Transformer), a unified transformer-based framework that integrates vertebra detection and foundation-model segmentation for generalizable spinal analysis. A Roboflow Detection Transformer (RF-DETR) with a DINOv2 backbone localizes vertebrae, followed by post-processing to ensure anatomical continuity. The resulting bounding boxes serve as automatic prompts for a fine-tuned Segment Anything Model 2.1 (SAM 2.1), which generates high-resolution vertebral masks. Cobb angles are then computed from vertebral masks, enabling estimation of both main and compensatory curves. Evaluations on the in-house (TVGH-SpineXR) and external (SpineWeb-16) datasets demonstrate encouraging performance on both internal and external datasets, achieving mean Dice coefficients of 0.944 and 0.781, respectively, and mean absolute Cobb angle errors of approximately 2–3° in-domain and 4.93° under cross-domain evaluation. Despite being trained solely on TVGH-SpineXR, DGPDT maintains accuracy comparable to models trained directly on the benchmark dataset. By coupling detection-guided prompting with transformer-based segmentation, DGPDT achieves a clinically acceptable mean absolute error (<5°), suggesting good reproducibility and potential applicability beyond the training dataset.

@article{lu2026dgpdt,
  title={DGPDT: Detection-Guided and Prompt-Driven Transformers for Automated and Generalizable Cobb Angle Estimation},
  author={Lu, Chih-Yi and Tu, Chen-Hung and Hsieh, Chun-Yi and Feng, Chi-Kuang and Hsieh, I-Yun Lisa},
  journal={IEEE Transactions on Medical Imaging},
  year={2026},
  month=jun,
  doi={10.1109/TMI.2026.3701624}
}