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 (). 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.

中文翻译：

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基于边缘分割算法的超声图像治疗甲状腺结节中喉返神经损伤的预防

本研究旨在探讨不同算法对甲状腺结节超声图像的分割效果，以更好地保护甲状腺手术中的喉返神经。具体选取甲状腺结节患者186例作为研究对象。从图像分割效果、像素精度（PA）、交集比联合（IOU）值、算法运行时间、术后喉返神经6个方面比较梯度向量流（GVF）Snake、Watershed和Snake算法的分割性能伤、术中出血量和术后引流量。发现GVF Snake算法的平均PA值（0.954）和IOU值（0.866）明显高于其他两种算法。基于GVF Snake算法的喉返神经损伤总发生率（4.69%）明显低于Snake算法（19.35%）和分水岭算法（16.13%）。基于 GVF Snake 算法的出血量和术后引流量小于其他两种算法（）。综上所述，GVF Snake算法表现出理想的分割效果，建议在甲状腺结节的治疗中更好地保​​护喉返神经。