  The scoliosis progression in adolescents requires close monitoring to timely
take treatment measures. Ultrasound imaging is a radiation-free and low-cost
alternative in scoliosis assessment to X-rays, which are typically used in
clinical practice. However, ultrasound images are prone to speckle noises,
making it challenging for sonographers to detect bony features and follow the
spine's curvature. This paper introduces a robotic-ultrasound approach for
spinal curvature tracking and automatic navigation. A fully connected network
with deconvolutional heads is developed to locate the spinous process
efficiently with real-time ultrasound images. We use this machine
learning-based method to guide the motion of the robot-held ultrasound probe
and follow the spinal curvature while capturing ultrasound images and
correspondent position. We developed a new force-driven controller that
automatically adjusts the probe's pose relative to the skin surface to ensure a
good acoustic coupling between the probe and skin. After the scanning, the
acquired data is used to reconstruct the coronal spinal image, where the
deformity of the scoliosis spine can be assessed and measured. To evaluate the
performance of our methodology, we conducted an experimental study with human
subjects where the deviations from the image center during the robotized
procedure are compared to that obtained from manual scanning. The angles of
spinal deformity measured on spinal reconstruction images were similar for both
methods, implying that they equally reflect human anatomy.
