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

Published on Dec 5, 2020in Physics in Medicine and Biology3.609
路 DOI :10.1088/1361-6560/ABA87C
Zhan Wu (SEU: Southeast University), Rongjun Ge6
Estimated H-index: 6
(SEU: Southeast University)
+ 5 AuthorsHengyong Yu39
Estimated H-index: 39
(University of Massachusetts Lowell)
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. 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.
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