MD-NDNet: multi-dimension convolutional neural network for false positive reduction in pulmonary nodule detection.

MD-NDNet: multi-dimension convolutional neural network for false positive reduction in pulmonary nodule detection. Phys Med Biol. 2020 Jul 22;: Authors: Wu Z, Ge R, Shi G, Zhang L, Chen Y, Luo L, Cao Y, Yu H Abstract Pulmonary nodule false positive reduction is of great significance for automated nodule detection in clinical diagnosis of low-dose computerized tomography (LDCT) lung cancer screening. Due to individual intra-nodule variations and visual similarities between true nodules and false positives as soft tissues in the 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-dimension nodule detection network (MD-NDNet) for automatic nodule false positive reduction using deep convolutional neural network (DCNNs). The underlying method collaboratively integrates multi-dimension nodule information to complementarily and comprehensively extract nodule inter-plane volumetric correlation features using three-dimension CNNs (3D CNNs) and spatial nodule correlation features from sagittal, coronal, and axial planes using two-dimension CNNs (2D CNNs) with attention module. To encompass 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. Experi...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research