【摘要】 从CT影像中检测肺结节在肺癌的早期诊断中至关重要,而肺结节假阳性的筛查是提高肺结节检测准确度的重要一步。为了从大量候选结节中快速准确地区分出真正的肺结节,设计了一个3D卷积神经网络(CNN)筛查肺结节假阳性。提出了网络模型,通过恒等映射和残差单元来加速模型训练,采用单连接路径重复利用特征并重组新特征。基于该模型的肺结节假阳性筛查方法,与基于2D CNN的方法相比,不仅可以省略数据切片步骤,而且能够充分利用CT影像的空间信息;与其他基于3DCNN的方法相比,具有参数量小、模型训练快的优点。该方法在LUNA16数据集中的假阳性筛查中取得了较高的敏感度。更多还原

【Abstract】 Pulmonary Nodule Detection is the most promising way in early detection of pulmonary cancer.False positive reduction is one of the most crucial steps for improving the accuracy in automatic pulmonary nodule detection. For quickly and accurately discriminate true nodules from a large number of candidates, a 3 D convolutional neural networks(CNN) is proposed for false positive reduction. In the proposed network, identity mapping and residual unit are adopted to accelerate network training. At the same time, single connected path is explored to get new features. Compared with 2 D CNN based pulmonary nodule detection methods, the proposed method based on the proposed 3 D CNN can take full advantage of space structure of CT. Compared with other 3 D CNN based pulmonary nodule detection methods, the proposed method has fewer parameters that can make the training very fast. Experimental results on the public LUNA16 dataset demonstrate superior performance of the proposed method.更多还原