  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.
