  Health professionals extensively use Two- Dimensional (2D) Ultrasound (US)
videos and images to visualize and measure internal organs for various purposes
including evaluation of muscle architectural changes. US images can be used to
measure abdominal muscles dimensions for the diagnosis and creation of
customized treatment plans for patients with Low Back Pain (LBP), however, they
are difficult to interpret. Due to high variability, skilled professionals with
specialized training are required to take measurements to avoid low
intra-observer reliability. This variability stems from the challenging nature
of accurately finding the correct spatial location of measurement endpoints in
abdominal US images. In this paper, we use a Deep Learning (DL) approach to
automate the measurement of the abdominal muscle thickness in 2D US images. By
treating the problem as a localization task, we develop a modified Fully
Convolutional Network (FCN) architecture to generate blobs of coordinate
locations of measurement endpoints, similar to what a human operator does. We
demonstrate that using the TrA400 US image dataset, our network achieves a Mean
Absolute Error (MAE) of 0.3125 on the test set, which almost matches the
performance of skilled ultrasound technicians. Our approach can facilitate next
steps for automating the process of measurements in 2D US images, while
reducing inter-observer as well as intra-observer variability for more
effective clinical outcomes.
