Purpose: Pulmonary nodule detection has great significance for early treating lung cancer and increasing patient survival. This work presents a novel automated computer-aided detection scheme for pulmonary nodules based on deep convolutional neural networks (DCNNs).
Methods: The proposed approach employs 3D DCNNs based on squeeze-and-excitation network and residual network (SE-ResNet) for pulmonary nodule candidate detection and false-positive reduction. Specifically, a 3D region proposal network with a U-Net-like structure is designed for detecting pulmonary nodule candidates. For the subsequent false-positive reduction, a 3D SE-ResNet-based classifier is presented to accurately discriminate the true nodules from candidates. The 3D SE-ResNet modules boost the representational power of the network by adaptively recalibrating channel-wise residual feature responses. Both models utilize 3D SE-ResNet modules to learn nodule features effectively and improve nodule detection performance.
Results: On the public available lung nodule analysis 2016 dataset with 888 scans included, the proposed method reaches high detection sensitivities of 93.6% and 95.7% at one and four false positives per scan, respectively. Meanwhile, the competition performance metric score of 0.904 is achieved. The proposed method has the capability to detect multi-size nodules, especially the extremely small nodules.
Conclusion: In this paper, a 3D DCNNs framework based on 3D SE-ResNet modules is proposed to detect pulmonary nodules in chest CT images accurately. Experimental results demonstrate superior effectiveness of the proposed approach in pulmonary nodule detection task.
Keywords: 3D squeeze-and-excitation network; Computer-aided detection; Deep learning; Pulmonary nodule.