MD-NDNet: a multi-dimensional convolutional neural network for false-positive reduction in pulmonary nodule detection. (1st December 2020)

• Record Type: Journal Article
• Authors: Wu, Zhan; Ge, Rongjun; Shi, Gonglei; Zhang, Lu; Chen, Yang; Luo, Limin; Cao, Yu; Yu, Hengyong
• Is Part Of: Physics in medicine & biology. Volume 65:Number 23(2020:Dec.)
• Publication Date: 2020-12-01
• Subjects: pulmonary nodule -- false-positive reduction -- deep convolutional neural networks (DCNNs) -- multi-dimensional nodule correlation features; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aba87c
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: 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.