  Automated Cobb angle estimation based on X-ray images plays an important role
in scoliosis diagnosis, treatment, and progression surveillance. The inadequate
feature extraction and the noise in X-ray images are the main difficulties of
automated Cobb angle estimation, and it is challenging to ensure that the
calculated Cobb angle meets clinical requirements. To address these problems,
we propose a Landmark-aware Network named LaNet with three components, Feature
Robustness Enhancement Module (FREM), Landmark-aware Objective Function (LOF),
and Cobb Angle Calculation Method (CACM), for automated Cobb angle estimation
in this paper. To enhance feature extraction, FREM is designed to explore
geometric and semantic constraints among landmarks, thus geometric and semantic
correlations between landmarks are globally modeled, and robust landmark-based
features are extracted. Furthermore, to mitigate the effect of background noise
on landmark localization, LOF is proposed to focus more on the foreground near
the landmarks and ignore irrelevant background pixels by exploiting category
prior information of landmarks. In addition, we also advance CACM to locate the
bending segments first and then calculate the Cobb angle within the bending
segment, which facilitates the calculation of the clinical standardized Cobb
angle. The experiment results on the AASCE dataset demonstrate that our
proposed LaNet can significantly improve the Cobb angle estimation performance
and outperform other state-of-the-art methods.
