DGPDT: Detection-Guided and Prompt-Driven Transformers for Automated and Generalizable Cobb Angle Estimation
Journal
IEEE Transactions on Medical Imaging
ISSN
02780062
Date Issued
2026
Author(s)
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.
Subjects
Adolescent idiopathic scoliosis
Automated Cobb angle estimation
Cross-domain generalization
Foundation model
Vertebra detection and segmentation
Publisher
Institute of Electrical and Electronics Engineers Inc.
Type
journal article