Zuo Wangxia, Zhou Fuqiang, He Yuzhu
The School of Instrumentation and Optoelectronics Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100083, China.
The College of Electrical Engineering, University of South China, Hengyang, 421001, Hunan, China.
J Digit Imaging. 2020 Aug;33(4):846-857. doi: 10.1007/s10278-020-00326-0.
Numerous lung nodule candidates can be produced through an automated lung nodule detection system. Classifying these candidates to reduce false positives is an important step in the detection process. The objective during this paper is to predict real nodules from a large number of pulmonary nodule candidates. Facing the challenge of the classification task, we propose a novel 3D convolution neural network (CNN) to reduce false positives in lung nodule detection. The novel 3D CNN includes embedded multiple branches in its structure. Each branch processes a feature map from a layer with different depths. All of these branches are cascaded at their ends; thus, features from different depth layers are combined to predict the categories of candidates. The proposed method obtains a competitive score in lung nodule candidate classification on LUNA16 dataset with an accuracy of 0.9783, a sensitivity of 0.8771, a precision of 0.9426, and a specificity of 0.9925. Moreover, a good performance on the competition performance metric (CPM) is also obtained with a score of 0.830. As a 3D CNN, the proposed model can learn complete and three-dimensional discriminative information about nodules and non-nodules to avoid some misidentification problems caused due to lack of spatial correlation information extracted from traditional methods or 2D networks. As an embedded multi-branch structure, the model is also more effective in recognizing the nodules of various shapes and sizes. As a result, the proposed method gains a competitive score on the false positive reduction in lung nodule detection and can be used as a reference for classifying nodule candidates.
通过自动肺结节检测系统可以生成大量的肺结节候选对象。对这些候选对象进行分类以减少假阳性是检测过程中的重要一步。本文的目标是从大量肺结节候选对象中预测真正的结节。面对分类任务的挑战，我们提出了一种新颖的3D卷积神经网络（CNN）来减少肺结节检测中的假阳性。这种新颖的3D CNN在其结构中嵌入了多个分支。每个分支处理来自不同深度层的特征图。所有这些分支在末端级联；因此，来自不同深度层的特征被组合起来以预测候选对象的类别。所提出的方法在LUNA16数据集的肺结节候选对象分类中获得了具有竞争力的分数，准确率为0.9783，灵敏度为0.8771，精确率为0.9426，特异性为0.9925。此外，在竞争性能指标（CPM）上也取得了良好的性能，得分为0.830。作为一个3D CNN，所提出的模型可以学习关于结节和非结节的完整三维判别信息，以避免由于传统方法或2D网络提取的空间相关信息不足而导致的一些误识别问题。作为一种嵌入式多分支结构，该模型在识别各种形状和大小的结节方面也更有效。因此，所提出的方法在减少肺结节检测中的假阳性方面获得了具有竞争力的分数，可作为对结节候选对象进行分类的参考。
