srinidhipy / rlnlocalization

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

RLNLocalization

This repository is the implementation of the cascaded network for Recurrent Laryngeal Nerve Localization by Haoran Dou in CISTIB at University of Leeds.

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework.
Haoran Dou, Luyi Han, Yushuang He, Jun Xu, Nishant Ravikumar, Ritse Mann, Alejandro F. Frangi, Pew-Thian Yap, Yunzhi Huang.
International Conference on Medical Image Computing and Computer Assisted Intervention, 2022. [arXiv]

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.

Usage

How to Run

1. Run python train.py to train the UNet for the segmentation of the CCA, thyroid, trachea.
2. Run python infer.py to obtain the coarse localization results of the RLN.
3. Run python statistic_test.py and python prior_localize.py to obtain the results of the Bayesian alignment.
4. Run python refine_train.py to train the multi-scale locator for the localization of RLN.
5. Run python refine infer.py to obtain the final localization results of RLN.

Results

Citation

If this work is helpful for you, please cite our paper as follows:

TBD