Deep visual nerve tracking in ultrasound images

Ultrasound-guided regional anesthesia (UGRA) becomes a standard procedure in surgical operations and pain management, offers the advantages of nerve localization, and provides region of interest anatomical structure visualization. Nerve tracking presents a crucial step for practicing UGRA and it is useful and important to develop a tool to facilitate this step. However, nerve tracking is a very challenging task that anesthetists can encounter due to the noise, artifacts, and nerve structure variability. Deep-learning has shown outstanding performances in computer vision task including tracking. Many deep-learning trackers have been proposed, where their performance depends on the application. While no deep-learning study exists for tracking the nerves in ultrasound images, this paper explores thirteen most recent deep-learning trackers for nerve tracking and presents a comparative study for the best deep-learning trackers on different types of nerves in ultrasound images. We evaluate the performance of the trackers in terms of accuracy, consistency, time complexity, and handling different nerve situations, such as disappearance and losing shape information. Through the experimentation, certain conclusions were noted on deep learning trackers performance. Overall, deep-learning trackers provide good performance and show a comparative performance for tracking different kinds of nerves in ultrasound images.