False Positive Reduction of Pulmonary Nodules using Three-Channel Samples

摘要

We propose a novel method for false positive reduction of pulmonary nodules using three-channel samples with different average thickness. A three-channel sample contains a patch centered on the candidate point as well as two patches at the k-th slice above and below the candidate point. Three-channel samples include rich spatial contextual information of pulmonary nodules, and can be trained with a low computational and storage requirement. The convolutional neural networks (CNNs) are constructed and optimized as the feature extractor and classifier of candidates in our study. A fusion method is proposed for fusing multiple prediction results of each candidate. Our method reports high sensitivities of 84.8% and 91.4% at 4 and 8 false positives per scan respectively on 888 CT scans released by the LUNA16 Challenge. The experimental results show that our method significantly reduces false positives in pulmonary nodule detection.