基于深度混合卷积模型的肺结节检测方法

基于高维肺部计算机断层扫描（CT）图像的肺结节检测是一项极具挑战性的任务。在诸多肺结节检测算法中，深度卷积神经网络（CNN）最引人注目，其中二维（2D） CNN具有预训练模型多、检测效率高等优点，应用非常广泛，但肺结节本质是三维（3D）病灶，2D CNN会不可避免地造成信息损失，从而影响检测精度。3D CNN能充分利用CT图像空间信息，有效提升检测精度，但是3D CNN存在参数多、计算消耗大、过拟合风险高等不足。为了兼顾两者的优势，提出基于深度混合CNN的肺结节检测模型，通过在神经网络模型的浅层部署3D CNN，在模型的深层部署2D CNN，并增加反卷积模块，融合了多层级的图像特征，达到了在不损失检测精度的情况下减少模型参数、增强模型泛化能力，提高检测效率的目的。在LUNA16数据集上的实验结果表明，所提出的模型在平均每次扫描8个假阳性的情况下的敏感度为0.924，优于现有的先进模型。

Deep mixed convolution model for pulmonary nodule detection

Pulmonary nodule detection is a very challenging task based on high-dimensional lung Computed Tomography (CT) images. Among many pulmonary nodule detection algorithms, the deep Convolutional Neural Network (CNN) is the most attractive one. In this kind of networks, the Two-Dimensional (2D) CNNs with many pre-trained models and high detection efficiency are widely used. However, the nature of pulmonary nodules is the Three-Dimensional (3D) lesion, so that the 2D CNNs will inevitably cause information loss and thereby affect the detection accuracy. The 3D CNNs can make full use of the spatial information of CT images and effectively improve the detection accuracy, but the 3D CNNs have shortcomings such as many parameters, large calculation consumption and high risk of over fitting. In order to take the advantages of the two networks, a pulmonary nodule detection model based on a deep mixed CNN was proposed. By deploying 3D CNN in the shallow layer of the neural network model and 2D CNN in the deep layer of the model, and adding a deconvolution module to fuse multi-layer image features together, the model parameters were reduced and the generalization ability and the detection efficiency of the model were improved without decreasing the detection accuracy. Experimental results on LUNA16 dataset show that the proposed model has the sensitivity reached 0.924 under the condition of average 8 false positives per scan, which outperforms the existing state-of-the-art models.