  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.
