计算机科学 ›› 2018, Vol. 45 ›› Issue (1): 162-166.doi: 10.11896/j.issn.1002-137X.2018.01.028
赵鹏飞,赵涓涓,强彦,王峰智,赵文婷
ZHAO Peng-fei, ZHAO Juan-juan, QIANG Yan, WANG Feng-zhi and ZHAO Wen-ting
摘要： 针对传统计算机辅助诊断系统中肺部结节检出过程复杂,检出结果依赖于分类前期每个步骤的性能,以及存在假阳性率高的问题,提出了一种基于卷积神经网络的端到端的肺结节检测方法。该方法首先使用大量带标签的肺结节数据对构建的多输入卷积神经网络进行训练,实现从原始数据到语义标签的有监督学习。然后采用快速边缘检测方法和二维高斯概率密度函数构建候选区域模板,从待检测CT序列中获取候选区域并将其作为多输入卷积神经网络的输入数据。最后采用判定阈值实现疑似肺结节区域标注,同时在相邻的CT影像中进行重点检测。在LIDC-IDRI数据集上的大量实验结果表明,所提方法在肺部CT影像中对微、小结节的检出率较高；同时,重点检测模板能够小幅降低微、小结节检测的假阳率。
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