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 3 mm, 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.
H. Dou and L. Han contributed equally to this work.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant No. 62101365 and the startup foundation of Nanjing University of Information Science and Technology. Haoran Dou was funded by the Royal Academy of Engineering Chair in Emerging Technologies Scheme (CiET1819/19). Luyi Han was funded by Chinese Scholarship Council (CSC) scholarship. Alejandro F. Frangi was funded by the Royal Academy of Engineering (INSILEX CiET1819/19), Engineering and Physical Sciences Research Council (EPSRC) programs TUSCA EP/V04799X/1, and the Royal Society Exchange Programme CROSSLINK IES\(\backslash \)NSFC\(\backslash \)201380. Jun Xu was funded by the National Natural Science Foundation of China (Nos. U1809205, 62171230, 92159301, 61771249, 91959207, 81871352).
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Dou, H. et al. (2022). Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_25
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