Dou, H., Han, L., He, Y. et al. (6 more authors) (2022) Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, Part IV. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, 18-22 Sep 2022, Singapore. Lecture Notes in Computer Science, 13434 . Springer , pp. 258-267. ISBN 9783031164392
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Bayesian shape alignment; Recurrent laryngeal nerve; Localization in ultrasound |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Sep 2023 12:26 |
Last Modified: | 01 Sep 2023 12:26 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-031-16440-8_25 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202910 |