基于多尺度卷积的新型肺结节位置检测方法

Novel Pulmonary Nodule Position Detection Method Based on Multiscale Convolution

目的 为了改善目前基于CT影像的肺结节位置检测方法准确性不高,减少漏检或误检问题,有效辅助影像科医生进行肺结节诊断。方法 提出一种基于多尺度卷积的新型肺结节位置检测方法。首先,采用影像预处理方法消除肺部CT影像中的噪声和伪影现象;其次,选取相邻位置的多幅单帧CT影像拼接成多帧图像,通过多尺度卷积改进的人工神经网络模型U-Net进行特征提取,增强了对不同尺寸和形状肺结节的特征提取能力,提高肺结节特征提取的准确性;最后,采用结节点检测方法对U-Net训练过程的损失函数进行改进,提高了肺结节位置检测准确性。结果 在LIDC-IDRI数据集上进行试验,实验结果表明该方法对≥3 mm和小于3 mm的肺结节检测的准确性分别达到98.02%和96.94%。结论 该方法可有效提高CT图像序列的肺结节检测准确性,能够较好满足肺结节诊断需求。

Objective In order to improve the accuracy of the current pulmonary nodule location detection method based on CT images,reduce the problem of missed detection or false detection,and effectively assist imaging doctors in the diagnosis of pulmonary nodules.Methods Propose a novel method for detecting the location of pulmonary nodules based on multiscale convolution.First,image preprocessing methods are used to eliminate the noise and artifacts in lung CT images.Second,multiple adjacent single-frame CT images are selected to be concatenate into multi-frame images,and the feature extraction is carried out through the artificial neural network model U-Net improved by multi-scale convolution to enhanced feature extraction capability for pulmonary nodules of different sizes and shapes,so as to improve the accuracy of feature extraction of pulmonary nodules.Finally,using point detection to improve the loss function of U-Net training process,the accuracy of pulmonary nodule location detection is improved.Results The accuracy of detecting pulmonary nodules equal or larger than 3 mm and smaller than 3 mm are 98.02% and 96.94% respectively.Conclusion This method can effectively improve the detection accuracy of pulmonary nodules on CT image sequence,and can better meet the diagnostic needs of pulmonary nodules.