Pulmonary nodule false-positive reduction is of great significance for automated nodule detection in clinical diagnosis of low-dose computed tomography (LDCT) lung cancer screening. Due to individual intra-nodule variations and visual similarities between true nodules and false positives as soft tissues in LDCT images, the current clinical practices remain subject to shortcomings of potential high-risk and time-consumption issues. In this paper, we propose a multi-dimensional nodule detection network (MD-NDNet) for automatic nodule false-positive reduction using deep convolutional neural network (DCNNs). The underlying method collaboratively integrates multi-dimensional nodule information to complementarily and comprehensively extract nodule inter-plane volumetric correlation features using three-dimensional CNNs (3D CNNs) and spatial nodule correlation features from sagittal, coronal, and axial planes using two-dimensional CNNs (2D CNNs) with attention module. To incorporate different sizes and shapes of nodule candidates, a multi-scale ensemble strategy is employed for probability aggregation with weights. The proposed method is evaluated on the LUNA16 challenge dataset in ISBI 2016 with ten-fold cross-validation. Experiment results show that the proposed framework achieves classification performance with a CPM score of 0.9008. All of these indicate that our method enables an efficient, accurate and reliable pulmonary nodule detection for clinical diagnosis.

肺结节假阳性的减少对于自动结节检测在低剂量计算机断层扫描（LDCT）肺癌筛查的临床诊断中具有重要意义。由于LDCT图像中的单个结节内变异和真实结节与假阳性（作为软组织）之间的视觉相似性，当前的临床实践仍然存在潜在的高风险和耗时问题的缺点。在本文中，我们提出了使用深度卷积神经网络（DCNN）进行自动结节假阳性归纳的多维结节检测网络（MD-NDNet）。底层方法协同集成多维结节信息，以使用三维CNN（3D CNN）互补和全面地提取结节平面间体积相关特征，并使用二维CNN从矢状，冠状和轴状平面中提取空间结节相关特征（ 2D CNN）和关注模块。为了合并不同大小和形状的结节候选，采用多尺度集合策略进行权重概率聚集。该方法在ISBI 2016的LUNA16挑战数据集上进行了十倍交叉验证。实验结果表明，所提出的框架以0.9008的CPM得分实现了分类性能。所有这些都表明我们的方法可以实现高效，