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.

[1]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Anselmo Cardoso de Paiva,et al.  Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index , 2014, Artif. Intell. Medicine.

[4]  Tae-Sun Choi,et al.  Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor , 2014, Comput. Methods Programs Biomed..

[5]  Wei Li,et al.  Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images , 2016, Comput. Math. Methods Medicine.

[6]  He Ma,et al.  An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network , 2018, IEEE Journal of Biomedical and Health Informatics.

[7]  Li Feng,et al.  Improving dynamic contrast-enhanced MRI of the lung using motion-weighted sparse reconstruction: Initial experiences in patients. , 2020, Magnetic resonance imaging.

[8]  Qi Liu,et al.  Automatic nodule detection for lung cancer in CT images: A review , 2018, Comput. Biol. Medicine.

[9]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[10]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[12]  Wenqing Sun,et al.  Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks , 2019, Comput. Medical Imaging Graph..

[13]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Guy Engelhard,et al.  Lung Nodules Detection and Segmentation Using 3D Mask-RCNN , 2019, ArXiv.

[15]  H. Kauczor,et al.  Screening for lung cancer: Does MRI have a role? , 2017, European journal of radiology.

[16]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[17]  Wenkai Huang,et al.  A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning , 2019, PloS one.

[18]  H. Miller.  The FROC curve: a representation of the observer's performance for the method of free response. , 1969, The Journal of the Acoustical Society of America.

[19]  Vivek Vaidya,et al.  Lung nodule detection in CT using 3D convolutional neural networks , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[20]  W. Sanderson,et al.  A case-control study of trace-element status and lung cancer in Appalachian Kentucky , 2019, PloS one.

[21]  Na Yang,et al.  Lung Nodule Detection With Deep Learning in 3D Thoracic MR Images , 2019, IEEE Access.

[22]  Kaiming He,et al.  Group Normalization , 2018, ECCV.

[23]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[24]  Hong Liu,et al.  Multi-Branch Ensemble Learning Architecture Based on 3D CNN for False Positive Reduction in Lung Nodule Detection , 2019, IEEE Access.

[25]  Berkman Sahiner,et al.  3D convolutional neural network for automatic detection of lung nodules in chest CT , 2017, Medical Imaging.

[26]  Tao Zhou,et al.  Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs , 2018, Comput. Biol. Medicine.

[27]  Bin Zheng,et al.  Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.