# Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression

arXiv: https://arxiv.org/abs/2012.12626 · HTML: https://mlsanity.com/paper/2012.12626/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression](https://mlsanity.com/paper/2208.12462/) (2022)
- [Feature Aggregation and Refinement Network for 2D AnatomicalLandmark Detection](https://mlsanity.com/paper/2111.00659/) (2021)
- [Effective and Interpretable fMRI Analysis via Functional Brain Network Generation](https://mlsanity.com/paper/2107.11247/) (2021)
- [Model-based multi-parameter mapping](https://mlsanity.com/paper/2102.01604/) (2021)
- [Automatic segmentation of vertebral features on ultrasound spine images using Stacked Hourglass Network](https://mlsanity.com/paper/2105.03847/) (2021)

## Abstract (arXiv)

The Cobb angle that quantitatively evaluates the spinal curvature plays an important role in the scoliosis diagnosis and treatment. Conventional measurement of these angles suffers from huge variability and low reliability due to intensive manual intervention. However, since there exist high ambiguity and variability around boundaries of vertebrae, it is challenging to obtain Cobb angles automatically. In this paper, we formulate the estimation of the Cobb angles from spinal X-rays as a multi-output regression task. We propose structured support vector regression (S^2VR) to jointly estimate Cobb angles and landmarks of the spine in X-rays in one single framework. The proposed S^2VR can faithfully handle the nonlinear relationship between input images and quantitative outputs, while explicitly capturing the intrinsic correlation of outputs. We introduce the manifold regularization to exploit the geometry of the output space. We propose learning the kernel in S2VR by kernel target alignment to enhance its discriminative ability. The proposed method is evaluated on the spinal X-rays dataset of 439 scoliosis subjects, which achieves the inspiring correlation coefficient of 92.76% with ground truth obtained manually by human experts and outperforms two baseline methods. Our method achieves the direct estimation of Cobb angles with high accuracy, which indicates its great potential in clinical use.