Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

TL;DR

This paper introduces a Bayesian shape framework for accurately localizing the tiny recurrent laryngeal nerve in ultrasound images, aiding safer robotic thyroidectomy procedures.

Contribution

It presents a knowledge-driven, Bayesian shape alignment method that improves RLN localization accuracy by modeling anatomical relationships and using a dual-path neural network.

Findings

Achieves higher hit rates than existing methods

Reduces distance errors significantly

Demonstrates robustness in ultrasound RLN detection

Abstract

Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy. Ultrasound (US) is a viable alternative for RLN detection due to its safety and ability to provide real-time feedback. However, the tininess of the RLN, with a diameter typically less than 3mm, poses significant challenges to the accurate localization of the RLN. In this work, we propose a knowledge-driven framework for RLN localization, mimicking the standard approach surgeons take to identify the RLN according to its surrounding organs. We construct a prior anatomical model based on the inherent relative spatial relationships between organs. Through Bayesian shape alignment (BSA), we obtain the candidate coordinates of the center of a region of interest (ROI) that encloses the RLN. The ROI allows a decreased field of view for determining the refined centroid of the RLN using a dual-path identification network, based on multi-scale semantic information. Experimental results indicate that the proposed method achieves superior hit rates and substantially smaller distance errors compared with state-of-the-art methods.