.png){kind=icon}
.png){kind=icon}
Return
DGPDT: Detection-Guided and Prompt-Driven Transformers for Automated and Generalizable Cobb Angle Estimation
.png){kind=icon}
PRE
AI
C
C
C
C
I
DOI：10.1109/tmi.2026.3701624.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lt 5^{\circ }\text {)}$ </tex-math></inline-formula>, suggesting good reproducibility and potential applicability beyond the training dataset.
Keywords:
Adolescent idiopathic scoliosis
automated cobb angle estimation
vertebra detection and segmentation
foundation model
cross-domain generalization
Journal
IF:
9.8
Papers:
6.2K
Citations:
3.7W
Organization
Cited Papers