Journal
PHYSICS IN MEDICINE AND BIOLOGY
Volume 65, Issue 23, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1361-6560/aba87c
Keywords
pulmonary nodule; false-positive reduction; deep convolutional neural networks (DCNNs); multi-dimensional nodule correlation features
Funding
- State's Key Project of Research and Development Plan [2017YFA0104302, 2017YFC0109202, 2017YFC0107900]
- National Natural Science Foundation [61801003, 61871117, 81471752]
- China Scholarship Council [201906090145]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available