Salient detection network for lung nodule detection in 3D Thoracic MRI Images

Abstract

The detection of lung nodule in magnetic resonance imaging (MRI) images is the key to the diagnosis of lung cancer. However, few computer-aided diagnosis (CAD) methods have been carried out for lung nodule detection in MRI images. Moreover, there is a challenge that small nodules are easily missed. In this paper, a salient detection network (SDN) is proposed for nodule detection in thoracic MRI images. To effectively aggregate multi-level feature maps and improve the detection rate of small nodules, an encoding-decoding block is designed, which output the salient detection map (SDM). To highlight the whole nodule region and suppress the distraction regions, a cascade salient map generation scheme is constructed by bifurcated VGG16 network, which employs the initial salient map to refine feature maps. Finally, one false positive (FP) reduction criterion is adopted to reduce FPs and preserve true nodules. The proposed method is tested on 142 T2-weighted MRI scans from the First Affiliated Hospital of Guangzhou Medical University. The true positive rate (TPR) of the proposed method is 94.0 % with 7.19 FPs/scan. Experimental results demonstrate that the proposed SDN can reduce the dependence on nodule size and improve the detection rate for small nodules. It shows better performance compared with existing nodule detection methods.