Nerve Block Target Localization and Needle Guidance for Autonomous Robotic Ultrasound Guided Regional Anesthesia

TL;DR

This paper presents a comprehensive system for autonomous ultrasound-guided regional anesthesia, including nerve segmentation, target localization, and needle guidance, validated on a large dataset with promising accuracy.

Contribution

The authors developed a large annotated US image dataset, novel models for nerve and needle segmentation, and an integrated guidance system for autonomous anesthesia procedures.

Findings

Nerve segmentation model achieved high accuracy across different US scanners.

Needle trajectory prediction error was within 5 mm, comparable to expert performance.

Public release of the dataset facilitates further research in autonomous US-guided procedures.

Abstract

Visual servoing for the development of autonomous robotic systems capable of administering UltraSound (US) guided regional anesthesia requires real-time segmentation of nerves, needle tip localization and needle trajectory extrapolation. First, we recruited 227 patients to build a large dataset of 41,000 anesthesiologist annotated images from US videos of brachial plexus nerves and developed models to localize nerves in the US images. Generalizability of the best suited model was tested on the datasets constructed from separate US scanners. Using these nerve segmentation predictions, we define automated anesthesia needle targets by fitting an ellipse to the nerve contours. Next, we developed an image analysis tool to guide the needle toward their targets. For the segmentation of the needle, a natural RGB pre-trained neural network was first fine-tuned on a large US dataset for domain transfer and then adapted for the needle using a small dataset. The segmented needle trajectory angle is calculated using Radon transformation and the trajectory is extrapolated from the needle tip. The intersection of the extrapolated trajectory with the needle target guides the needle navigation for drug delivery. The needle trajectory average error was within acceptable range of 5 mm as per experienced anesthesiologists. The entire dataset has been released publicly for further study by the research community at https://github.com/Regional-US/