Automated pulmonary nodule detection in CT images using deep convolutional neural networks. (January 2019)

• Record Type: Journal Article
• Authors: Xie, Hongtao; Yang, Dongbao; Sun, Nannan; Chen, Zhineng; Zhang, Yongdong
• Is Part Of: Pattern recognition. Volume 85(2019:Jan.)
• Page Start: 109
• Page End: 119
• Publication Date: 2019-01
• Subjects: Nodule detection -- Convolutional neural network -- False positive reduction -- Computer-aided diagnosis; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.07.031
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Lung cancer is one of the leading causes of cancer-related death worldwide. Early diagnosis can effectively reduce the mortality, and computer-aided diagnosis (CAD) as an important way to assist doctors has developed rapidly. In particular, automated pulmonary nodule detection in computed tomography (CT) images is crucial to CAD. It is a challenging task to quickly locate the exact positions of lung nodules. In this paper, a novel automated pulmonary nodule detection framework with 2D convolutional neural network (CNN) is proposed to assist the CT reading process. Firstly, we adjust the structure of Faster R-CNN with two region proposal networks and a deconvolutional layer to detect nodule candidates, and then three models are trained for three kinds of slices for later result fusion. Secondly, a boosting architecture based on 2D CNN is designed for false positive reduction, which is a classifier to distinguish true nodules from the candidates. The misclassified samples are still kept for retraining a model which boosts the sensitivity for pulmonary nodule detection. Finally, the results of these networks are fused to vote out the final classification results. Extensive experiments are conducted on LUNA16, and the sensitivity of nodule candidate detection achieves 86.42%. For the false positive reduction, the sensitivity reaches 73.4% and 74.4% at 1/8 and 1/4 FPs/scan, respectively. It illustrates that the proposed method can obviously achieve accurate pulmonary nodule detection.