Edge Segmentation Algorithm‐Based Prevention of Recurrent Laryngeal Nerve Injury in Treatment of Thyroid Nodules Using Ultrasound Images

Jiyuan Wang,Jie Jin,Hanqing Zou,Shaokun Sun,Wenqian Xu,Gustavo Ramirez

doi:10.1155/2021/6583110

Abstract

The study was aimed to explore the segmentation effects of different algorithms on thyroid nodule ultrasound images, so as to better protect the recurrent laryngeal nerve during thyroid surgery. Specifically, 186 patients with thyroid nodules were selected as the research objects. The segmentation performances of the gradient vector flow (GVF) Snake, Watershed, and Snake algorithms were compared from 6 aspects of image segmentation effects, pixel accuracy (PA), Intersection over Union (IOU) value, algorithm running time, postoperative recurrent laryngeal nerve injury, intraoperative bleeding volume, and postoperative drainage volume. It was found that the average PA value (0.954) and the IOU value (0.866) of the GVF Snake algorithm were obviously higher than those of the other two algorithms. The total incidence of recurrent laryngeal nerve injury based on the GVF Snake algorithm (4.69%) was obviously lower than that of the Snake algorithm (19.35%) and the watershed algorithm (16.13%). The bleeding volume and postoperative drainage volume based on the GVF Snake algorithm were less versus the other two algorithms ( P < 0.05 ). In conclusion, the GVF Snake algorithm demonstrates ideal segmentation effects, which is suggested in the treatment of thyroid nodules to better protect the recurrent laryngeal nerve.

Highlights

Academic Editor: Gustavo Ramirez e study was aimed to explore the segmentation effects of different algorithms on thyroid nodule ultrasound images, so as to better protect the recurrent laryngeal nerve during thyroid surgery
186 patients with thyroid nodules were selected as the research objects. e segmentation performances of the gradient vector flow (GVF) Snake, Watershed, and Snake algorithms were compared from 6 aspects of image segmentation effects, pixel accuracy (PA), Intersection over Union (IOU) value, algorithm running time, postoperative recurrent laryngeal nerve injury, intraoperative bleeding volume, and postoperative drainage volume
It was found that the average PA value (0.954) and the IOU value (0.866) of the GVF Snake algorithm were obviously higher than those of the other two algorithms. e total incidence of recurrent laryngeal nerve injury based on the GVF Snake algorithm (4.69%) was obviously lower than that of the Snake algorithm (19.35%) and the watershed algorithm (16.13%). e bleeding volume and postoperative drainage volume based on the GVF Snake algorithm were less versus the other two algorithms (P < 0.05)

Summary

Introduction

Academic Editor: Gustavo Ramirez e study was aimed to explore the segmentation effects of different algorithms on thyroid nodule ultrasound images, so as to better protect the recurrent laryngeal nerve during thyroid surgery. The GVF Snake algorithm demonstrates ideal segmentation effects, which is suggested in the treatment of thyroid nodules to better protect the recurrent laryngeal nerve. As a common complication of thyroid surgery, recurrent laryngeal nerve injury has a reported incidence of about 0.3% to 15.4%. E clinical diagnosis reveals that the most likely location for recurrent laryngeal nerve injury is 2 cm before the larynx, which is adjacent to the thyroid gland, covered by Zuckerkandl nodules and surrounded by small branches of the thyroid arteries and veins and vascular fascia. The traditional Snake algorithm is not easy to converge concave images, and the range of action is small, and it may converge to the local minimum [14]. e watershed algorithm uses the similarity of adjacent pixels on the image as a reference basis to segment the image, but due to the presence of noise or other interference factors on the real image, the watershed algorithm is likely to be oversegmented [15]. erefore, improved algorithms such as the GVF Snake algorithm essentially have the ability to process concave images, but it has not yet been confirmed in the segmentation of thyroid nodules

Methods

Results

Conclusion