In this paper, an automated and visually explainable system is proposed for a scoliosis
assessment from spinal radiographs, which deals with the drawback of manual measurements, which are
known to be time-consuming, cumbersome, and operator dependent. Deep learning techniques have been
successfully applied in the accurate extraction of Cobb angle measurements, which is the gold standard
for a scoliosis assessment. Such deep learning methods directly estimate the Cobb angle without providing
structural information of the spine which can be used for diagnosis. Although conventional segmentationbased
methods can provide the spine structure, they still have limitations in the accurate measurement of the
Cobb angle. It would be desirable to build a clinician-friendly diagnostic system for scoliosis that provides
not only an automated Cobb angle assessment but also local and global structural information of the spine.
This paper addresses this need through the development of a hierarchical method which consisting of three
major parts. (1) A confidence map is used to selectively localize and identify all vertebrae in an accurate
and robust manner, (2) vertebral-tilt field is used to estimate the slope of an individual vertebra, and (3) the
Cobb angle is determined by combining the vertebral centroids with the previously obtained vertebral-tilt
field. The performance of the proposed method was validated, resulting in circular mean absolute error of
3:51 and symmetric mean absolute percentage error of 7:84% for the Cobb angle.