A cascaded dual-pathway residual network for lung nodule segmentation in CT images


      • For small and juxtapleural nodules, our method can achieve attractive performance.
      • Extract multi-view and multi-scale features of nodules by a cascaded structure.
      • A dual-pathway architecture based on the residual block was proposed.
      • We propose an improved weighted sampling strategy that can adequately sample small nodules.


      It is difficult to obtain an accurate segmentation due to the variety of lung nodules in computed tomography (CT) images. In this study, we propose a data-driven model, called the Cascaded Dual-Pathway Residual Network (CDP-ResNet) to improve the segmentation of lung nodules in the CT images. Our approach incorporates the multi-view and multi-scale features of different nodules from CT images. The proposed residual block based dual-path network extracts local features and rich contextual information of lung nodules. In addition, we designed an improved weighted sampling strategy to select training samples based on the edge. The proposed method was extensively evaluated on an LIDC dataset, which contains 986 nodules. Experimental results show that the CDP-ResNet achieves superior segmentation performance with an average DICE score (standard deviation) of 81.58% (11.05) on the LIDC dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison shows that the CDP-ResNet is slightly better than human experts in terms of segmentation accuracy. Meanwhile, the proposed segmentation method outperforms existing methods.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Physica Medica: European Journal of Medical Physics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Ferlay J.
        • Soerjomataram I.
        • Dikshit R.
        • Eser S.
        • Mathers C.
        • Rebelo M.
        • et al.
        Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.
        Int J Cancer. 2015; 136: E359-E386
        • Bhavanishankar K.
        • Sudhamani M.V.
        Techniques for detection of solitary pulmonary nodules in human lung and their classifications -a Survey.
        Int J Cybern Inf. 2015; 4: 27-40
        • Siegel R.L.
        • Miller K.D.
        • Jemal A.
        Cancer statistics, 2018.
        CA Cancer J Clin. 2018; 68: 7-30
        • Aerts H.J.W.L.
        • Velazquez E.R.
        • Leijenaar R.T.H.
        • Parmar C.
        • Grossmann P.
        • Carvalho S.
        • et al.
        Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
        Nat Commun. 2014; 5: 4006
        • Reeves A.P.
        • Chan A.B.
        • Yankelevitz D.F.
        • Henschke C.I.
        • Kressler B.
        • Kostis W.J.
        On measuring the change in size of pulmonary nodules.
        IEEE Trans Med Imaging. 2006; 25: 435-450
        • MacMahon H.
        • Austin J.H.M.
        • Gamsu G.
        • Herold C.J.
        • Jett J.R.
        • Naidich D.P.
        • et al.
        Guidelines for management of small pulmonary nodules detected on CT Scans: a statement from the fleischner society.
        Radiology. 2005; 237: 395-400
        • Kostis W.J.
        • Reeves A.P.
        • Yankelevitz D.F.
        • Henschke C.I.
        Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images.
        IEEE Trans Med Imaging. 2003; 22: 1259-1274
        • Gonçalves L.
        • Novo J.
        • Campilho A.
        Hessian based approaches for 3D lung nodule segmentation.
        Expert Syst Appl. 2016; 61: 1-15
        • Sargent D.
        • Park S.Y.
        Semi-automatic 3D lung nodule segmentation in CT using dynamic programming.
        Proc SPIE. 2017; : 10133
        • Kubota T.
        • Jerebko A.K.
        • Dewan M.
        • Salganicoff M.
        • Krishnan A.
        Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models.
        Med Image Anal. 2011; 15: 133-154
        • Dehmeshki J.
        • Amin H.
        • Valdivieso M.
        • Ye X.
        Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach.
        IEEE Trans Med Imaging. 2008; 27: 467-480
        • Farag A.A.
        • Munim H.E.A.E.
        • Graham J.H.
        • Farag A.A.
        A novel approach for lung nodules segmentation in chest CT using level sets.
        IEEE Trans Image Process. 2013; 22: 5202-5213
        • Ye X.
        • Beddoe G.
        • Slabaugh G.G.
        Automatic graph cut segmentation of lesions in CT using mean shift superpixels.
        Int J Biomed Imaging. 2010; 2010: 983963:1-983963:14
        • Chan T.F.
        • Vese L.A.
        Active contours without edges.
        IEEE Trans Image Process. 2001; 10: 266-277
        • Nithila E.E.
        • Kumar S.S.
        Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clustering.
        Alexandria Eng J. 2016; 55: 2583-2588
        • Wang J.
        • Guo H.
        Automatic approach for lung segmentation with juxta-pleural nodules from thoracic ct based on contour tracing and correction.
        Comput Math Methods Med. 2016; 20162962047
      1. Mukherjee S, Huang X, Bhagalia RR. Lung nodule segmentation using deep learned prior based graph cut. In 2017 IEEE 14th Int. Symp. Biomed. Imaging (ISBI 2017), 2017, p. 1205–8. doi:10.1109/ISBI.2017.7950733.

        • Gui L.
        • Li C.
        • Yang X.
        Medical image segmentation based on level set and isoperimetric constraint.
        Phys Med. 2017; 42: 162-173
      2. Lu L, Barbu A, Wolf M, Liang J, Salganicoff M, Comaniciu D. Accurate polyp segmentation for 3D CT colongraphy using multi-staged probabilistic binary learning and compositional model. In 2008 IEEE Conf. Comput. Vis. Pattern Recognit., 2008, p. 1–8. doi:10.1109/CVPR.2008.4587423.

        • Lu L.
        • Bi J.
        • Wolf M.
        • Salganicoff M.
        Effective 3D object detection and regression using probabilistic segmentation features in CT images.
        CVPR. 2011; 2011: 1049-1056
        • Mukhopadhyay S.
        A segmentation framework of pulmonary nodules in lung CT images.
        J Digit Imaging. 2016; 29: 86-103
        • Shen S.
        • Bui A.A.T.
        • Cong J.
        • Hsu W.
        An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy.
        Comput Biol Med. 2015; 57: 139-149
      3. Wu D, Lu L, Bi J, Shinagawa Y, Boyer K, Krishnan A, et al. Stratified learning of local anatomical context for lung nodules in CT images. In 2010 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2010, p. 2791–8. doi:10.1109/CVPR.2010.5540008.

        • Messay T.
        • Hardie R.C.
        • Tuinstra T.R.
        Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset.
        Med Image Anal. 2015; 22: 48-62
        • Wang S.
        • Zhou M.
        • Liu Z.
        • Liu Z.
        • Gu D.
        • Zang Y.
        • et al.
        Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.
        Med Image Anal. 2017; 40: 172-183
      4. Wang S, Zhou M, Gevaert O, Tang Z, Dong D, Liu Z, et al. A multi-view deep convolutional neural networks for lung nodule segmentation. In 2017 39th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2017, p. 1752–5. doi:10.1109/EMBC.2017.8037182.

      5. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In 2015 IEEE Conf. Comput. Vis. Pattern Recognit., 2015, p. 3431–40. doi:10.1109/CVPR.2015.7298965.

        • Ronneberger O.
        • Fischer P.
        • Brox T.
        U-Net: convolutional networks for biomedical image segmentation BT – medical image computing and computer-assisted intervention – MICCAI.
        Springer International Publishing, Cham2015: 234-241
      6. Milletari F, Navab N, Ahmadi SA. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In 2016 Fourth Int. Conf. 3D Vis., 2016, p. 565–71. doi:10.1109/3DV.2016.79.

      7. He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In 2016 IEEE Conf. Comput. Vis. Pattern Recognit., 2016, p. 770–8. doi:10.1109/CVPR.2016.90.

        • Ioffe S.
        • Szegedy C.
        Batch normalization: accelerating deep network training by reducing internal covariate shift.
        in: BT – Proceedings of the 32nd International Conference on Machine Learning. ICML 2015, Lille, France2015: 448-456
      8. He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In 2015 IEEE Int. Conf. Comput. Vis., 2015, p. 1026–34. doi:10.1109/ICCV.2015.123.

        • Wang L.
        • Yang Y.
        • Min R.
        • Chakradhar S.
        Accelerating deep neural network training with inconsistent stochastic gradient descent.
        Neural Networks. 2017; 93: 219-229
      9. Caruana R, Lawrence S, Giles CL. Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping. In BT – Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA 2000:402–8.

        • Uijlings J.R.R.
        • van de Sande K.E.A.
        • Gevers T.
        • Smeulders A.W.M.
        Selective search for object recognition.
        Int J Comput Vis. 2013; 104: 154-171
        • Armato S.G.
        • McLennan G.
        • Bidaut L.
        • McNitt-Gray M.F.
        • Meyer C.R.
        • Reeves A.P.
        • et al.
        The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on CT scans.
        Med Phys. 2011; 38: 915-931
        • Setio A.A.A.
        • Ciompi F.
        • Litjens G.
        • Gerke P.
        • Jacobs C.
        • van Riel S.J.
        • et al.
        Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks.
        IEEE Trans Med Imaging. 2016; 35: 1160-1169
        • Setio A.A.A.
        • Traverso A.
        • de Bel T.
        • Berens M.S.N.
        • van den Bogaard C.
        • Cerello P.
        • et al.
        Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge.
        Med Image Anal. 2017; 42: 1-13
        • Havaei M.
        • Davy A.
        • Warde-Farley D.
        • Biard A.
        • Courville A.C.
        • Bengio Y.
        • et al.
        Brain tumor segmentation with deep neural networks.
        Med Image Anal. 2017; 35: 18-31
        • Valverde S.
        • Oliver A.
        • Roura E.
        • González-Villà S.
        • Pareto D.
        • Vilanova J.C.
        • et al.
        Automated tissue segmentation of MR brain images in the presence of white matter lesions.
        Med Image Anal. 2017; 35: 446-457
        • Gao Y.
        • Shao Y.
        • Lian J.
        • Wang A.Z.
        • Chen R.C.
        • Shen D.
        Accurate segmentation of ct male pelvic organs via regression-based deformable models and multi-task random forests.
        IEEE Trans Med Imaging. 2016; 35: 1532-1543
        • Shen W.
        • Zhou M.
        • Yang F.
        • Yu D.
        • Dong D.
        • Yang C.
        • et al.
        multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification.
        Pattern Recognit. 2017; 61: 663-673
        • Kang G.
        • Liu K.
        • Hou B.
        • Zhang N.
        3D multi-view convolutional neural networks for lung nodule classification.
        PLoS One. 2017; 12
        • Sun W.
        • Zheng B.
        • Qian W.
        Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.
        Comput Biol Med. 2017; 89: 530-539