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Classification and Diagnosis of Pulmonary Nodules in Thoracic Surgery Using CT Image Segmentation Algorithm
Scientific Programming ( IF 1.672 ) Pub Date : 2021-07-05 , DOI: 10.1155/2021/3367677
Degen Fang 1 , Chunlei Li 1 , Yanhong Ren 1
Affiliation  

This study was aimed at studying the pulmonary nodule (PN) classification and diagnosis through computed tomography (CT) images based on segmentation algorithms. 120 PN patients were taken as research subjects. Linear filter fine segmentation algorithm under 3D region growth was compared with the initial segmentation algorithm and applied to images of PN patients. The results showed that the segmentation effect of the proposed algorithm was at the upper-middle level. The cases of patients with smoking history were greatly more than those without (χ2 = 1.256, ). Benign and malignant PNs were classified, and morphological features included rough ones and round-like ones. The size characteristics included edge length and area. The gray-scale features included the uniformity of the gray-scale value and the mean value of the gray-scale value. The operation time of pulmonary lobectomy (76.2 ± 23.1 min) was obviously longer than that of pulmonary wedge resection (27.5.2 ± 4.5 min) (). The surgical blood loss of patients who underwent pulmonary lobectomy (125 ± 42 mL) was remarkably higher versus patients who underwent pulmonary wedge resection (51.6 ± 13.8 mL) (). After the operation, the length of stay of patients who underwent lobectomy (8.6 ± 1.4 days) was evidently longer than that of patients who underwent wedge resection (6.4 ± 1.2 days) (). The classification of benign and malignant PNs can effectively obtain the shape and size characteristics of PNs. Preoperative positioning surgery based on classification can shorten the operation time, reduce the amount of bleeding during the operation, and help improve the success rate of surgical resection.

中文翻译:

基于CT图像分割算法的胸外科肺结节分类诊断

本研究旨在通过基于分割算法的计算机断层扫描 (CT) 图像研究肺结节 (PN) 的分类和诊断。120 名 PN 患者作为研究对象。将3D区域增长下的线性滤波器精细分割算法与初始分割算法进行比较,并应用于PN患者的图像。结果表明,该算法的分割效果处于中上水平。患者有吸烟史的病例均大大超过那些没有(χ 2  = 1.256,)。PNs分为良恶性,形态特征包括粗糙的和圆形的。尺寸特征包括边长和面积。灰度特征包括灰度值的均匀性和灰度值的均值。肺叶切除术的手术时间(76.2±23.1 min)明显长于肺楔形切除术(27.5.2±4.5 min)()。接受肺叶切除术的患者的手术失血量 (125 ± 42 mL) 明显高于接受肺楔形切除术的患者 (51.6 ± 13.8 mL)。)。手术后,肺叶切除患者的住院时间(8.6±1.4天)明显长于楔形切除患者(6.4±1.2天)()。PNs的良恶性分类可以有效地获得PNs的形状和大小特征。基于分类的术前定位手术可以缩短手术时间,减少术中出血量,有助于提高手术切除的成功率。
更新日期:2021-07-05
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