生物医学工程研究2024，Vol.43Issue(5)：356-361,6.DOI:10.19529/j.cnki.1672-6278.2024.05.02
一种基于Res2Net改进的V-Net肺结节分割方法
A improved V-Net lung nodule segmentation method based on Res2Net
摘要
Abstract
In order to improve the segmentation efficiency of nodules in lung computed tomography(CT)images,we improved the V-Net 3D segmentation model and proposed the Res2Net-Vnet.Firstly,the image was preprocessed to alleviate the influence of une-ven positive and negative samples.Then,the original single-size convolution in V-Net was replaced with Res2Net convolution,and the scale feature of the receptive field was increased while reducing the model parameters by using small convolution kernel stacks.The ex-periments on Luna16 subdataset indicated that the Dice similarity coefficients of V-Net,SKV-Net and Res2Net-Vnet was 0.703,0.750 and 0.764,respectively.The results show that Res2Net-Vnet is superior to V-Net and SKV-Net in pulmonary nodule segmenta-tion,which proves the certain effectiveness in improving pulmonary nodule segmentation.关键词
肺癌/卷积网络/多尺度融合/残差连接/轻量级/辅助诊断Key words
Lung cancer/Convolutional network/Multi-scale fusion/Residual connection/Lightweight/Auxiliary diagnosis分类
医药卫生引用本文复制引用
李元龙,门靖茹,刘嘉林,陈琦,吴凉..一种基于Res2Net改进的V-Net肺结节分割方法[J].生物医学工程研究,2024,43(5):356-361,6.基金项目
山东第一医科大学(山东省医学科学院)国家级大学生创新创业训练项目(202310439111) （山东省医学科学院）
泰安市科技创新发展项目(2023NS381). （2023NS381）
