关键词: 卷积神经网络, 各向异性卷积, 肺结节分类, 计算机辅助诊断, 计算机断层成像（CT）

Abstract:

The diagnosis of pulmonary nodules in CT images plays an important role in the selection of therapeutic plan. the research tendency of deep learning based classification algorithm is to make full use of 3D information of CT images, Due to the differences in CT scanners and scanning protocols, different CT samples have different in-plane resolutions on slice thicknesses, which need additional preprocessing before feature extraction. Most of the literatures adopt interpolation method to unify the resolution, but this method will cause the image resolution reduction or the increased computation burden. To solve this problem, this paper proposes a 3D anisotropic convolution based neural network for pulmonary nodules classification. By dividing the standard 3D convolution into two kinds of 3D anisotropic convolutions of [k×k×1] and [1×1×k], it avoids directly applying 3D convolution to the original CT images and will not be affected by different image resolutions. This paper also proposes a crop-non-local pooling module to enable the shallow network to obtain global information. Experiments on Lung Image Database Consortium and Image Database Resource Initiative（LIDC-IDRI） dataset show that the proposed architecture can significantly reduce the parameters of network, improve the ability of extracting features. The accuracy, sensitivity and specificity of pulmonary nodules classification are 91.53%, 88.89% and 93.27% respectively.

Key words: convolutional neural networks, anisotropic convolution, pulmonary nodule classification, computer aided diagnosis systems, Computed Tomography（CT）