  The current clinical gold standard for evaluating adolescent idiopathic
scoliosis (AIS) is X-ray radiography, using Cobb angle measurement. However,
the frequent monitoring of the AIS progression using X-rays poses a challenge
due to the cumulative radiation exposure. Although 3D ultrasound has been
validated as a reliable and radiation-free alternative for scoliosis
assessment, the process of measuring spinal curvature is still carried out
manually. Consequently, there is a considerable demand for a fully automatic
system that can locate bony landmarks and perform angle measurements. To this
end, we introduce an estimation model for automatic ultrasound curve angle
(UCA) measurement. The model employs a dual-branch network to detect candidate
landmarks and perform vertebra segmentation on ultrasound coronal images. An
affinity clustering strategy is utilized within the vertebral segmentation area
to illustrate the affinity relationship between candidate landmarks.
Subsequently, we can efficiently perform line delineation from a clustered
affinity map for UCA measurement. As our method is specifically designed for
UCA calculation, this method outperforms other state-of-the-art methods for
landmark and line detection tasks. The high correlation between the automatic
UCA and Cobb angle (R$^2$=0.858) suggests that our proposed method can
potentially replace manual UCA measurement in ultrasound scoliosis assessment.
