  Objective: The spinous process angle (SPA) is one of the essential parameters
to denote three-dimensional (3-D) deformity of spine. We propose an automatic
segmentation method based on Stacked Hourglass Network (SHN) to detect the
spinous processes (SP) on ultrasound (US) spine images and to measure the SPAs
of clinical scoliotic subjects. Methods: The network was trained to detect
vertebral SP and laminae as five landmarks on 1200 ultrasound transverse images
and validated on 100 images. All the processed transverse images with
highlighted SP and laminae were reconstructed into a 3D image volume, and the
SPAs were measured on the projected coronal images. The trained network was
tested on 400 images by calculating the percentage of correct keypoints (PCK);
and the SPA measurements were evaluated on 50 scoliotic subjects by comparing
the results from US images and radiographs. Results: The trained network
achieved a high average PCK (86.8%) on the test datasets, particularly the PCK
of SP detection was 90.3%. The SPAs measured from US and radiographic methods
showed good correlation (r>0.85), and the mean absolute differences (MAD)
between two modalities were 3.3{\deg}, which was less than the clinical
acceptance error (5{\deg}). Conclusion: The vertebral features can be
accurately segmented on US spine images using SHN, and the measurement results
of SPA from US data was comparable to the gold standard from radiography.
