  Automated object recognition in medical images can facilitate medical
diagnosis and treatment. In this paper, we automatically segmented
supraclavicular nerves in ultrasound images to assist in injecting peripheral
nerve blocks. Nerve blocks are generally used for pain treatment after surgery,
where ultrasound guidance is used to inject local anesthetics next to target
nerves. This treatment blocks the transmission of pain signals to the brain,
which can help improve the rate of recovery from surgery and significantly
decrease the requirement for postoperative opioids. However, Ultrasound Guided
Regional Anesthesia (UGRA) requires anesthesiologists to visually recognize the
actual nerve position in the ultrasound images. This is a complex task given
the myriad visual presentations of nerves in ultrasound images, and their
visual similarity to many neighboring tissues. In this study, we used an
automated nerve detection system for the UGRA Nerve Block treatment. The system
can recognize the position of the nerve in ultrasound images using Deep
Learning techniques. We developed a model to capture features of nerves by
training two deep neural networks with skip connections: two extended U-Net
architectures with and without dilated convolutions. This solution could
potentially lead to an improved blockade of targeted nerves in regional
anesthesia.
