# B-Spine: Learning B-Spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation

arXiv: https://arxiv.org/abs/2310.09603 · HTML: https://mlsanity.com/paper/2310.09603/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation](https://mlsanity.com/paper/1805.04574/) (2018)
- [SSformer: A Lightweight Transformer for Semantic Segmentation](https://mlsanity.com/paper/2208.02034/) (2022)
- [Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression](https://mlsanity.com/paper/2208.12462/) (2022)
- [Binary segmentation of medical images using implicit spline representations and deep learning](https://mlsanity.com/paper/2102.12759/) (2021)
- [Deep Learning Parametrization for B-Spline Curve Approximation](https://mlsanity.com/paper/1807.08304/) (2018)
- [Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks](https://mlsanity.com/paper/1911.01126/) (2019)
- [Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces](https://mlsanity.com/paper/2110.12939/) (2021)

## Built on by (most cited first, 2 of 2)

- [Spline refinement with differentiable rendering](https://mlsanity.com/paper/2503.14525/) (2025)
- [Frequency-enhanced Multi-granularity Context Network for Efficient Vertebrae Segmentation](https://mlsanity.com/paper/2506.23086/) (2025)

## Abstract (arXiv)

Spinal curvature estimation is important to the diagnosis and treatment of the scoliosis. Existing methods face several issues such as the need of expensive annotations on the vertebral landmarks and being sensitive to the image quality. It is challenging to achieve robust estimation and obtain interpretable results, especially for low-quality images which are blurry and hazy. In this paper, we propose B-Spine, a novel deep learning pipeline to learn B-spline curve representation of the spine and estimate the Cobb angles for spinal curvature estimation from low-quality X-ray images. Given a low-quality input, a novel SegRefine network which employs the unpaired image-to-image translation is proposed to generate a high quality spine mask from the initial segmentation result. Next, a novel mask-based B-spline prediction model is proposed to predict the B-spline curve for the spine centerline. Finally, the Cobb angles are estimated by a hybrid approach which combines the curve slope analysis and a curve-based regression model. We conduct quantitative and qualitative comparisons with the representative and SOTA learning-based methods on the public AASCE2019 dataset and our new proposed CJUH-JLU dataset which contains more challenging low-quality images. The superior performance on both datasets shows our method can achieve both robustness and interpretability for spinal curvature estimation.