Abstract
We investigate the problem of diagnostic lung nodule classification using thoracic Computed Tomography (CT) screening. Unlike traditional studies primarily relying on nodule segmentation for regional analysis, we tackle a more challenging problem on directly modelling raw nodule patches without any prior definition of nodule morphology. We propose a hierarchical learning framework—Multi-scale Convolutional Neural Networks (MCNN)—to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. In particular, to sufficiently quantify nodule characteristics, our framework utilizes multi-scale nodule patches to learn a set of class-specific features simultaneously by concatenating response neuron activations obtained at the last layer from each input scale. We evaluate the proposed method on CT images from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), where both lung nodule screening and nodule annotations are provided. Experimental results demonstrate the effectiveness of our method on classifying malignant and benign nodules without nodule segmentation.
W. Shen and M. Zhou—These authors contributed equally.
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References
Aberle, D.R., Adams, A.M., Berg, C.D., Black, W.C., Clapp, J.D., Fagerstrom, R.M., Gareen, I.F., Gatsonis, C., Marcus, P.M., Sicks, J.: Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365(5), 395–409 (2011)
Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., Parmar, C., Grossmann, P., Cavalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Haibe-Kains, D., Rietveld, D., Hoebers, F., Rietbergen, M.M., Leemans, C.R., Dekker, A., Quackenbush, J., Gillies, R.J., Lambin, P.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, Article No. 4006 (2014). doi:10.1038/ncomms5006
Armato III, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., 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. 38(2), 915–931 (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005)
El-Baz, A., Nitzken, M., Khalifa, F., Elnakib, A., Gimel’farb, G., Falk, R., El-Ghar, M.A.: 3D shape analysis for early diagnosis of malignant lung nodules. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 772–783. Springer, Heidelberg (2011)
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2, 1735–1742 (2006)
Han, F., Zhang, G., Wang, H., Song, B., Lu, H., Zhao, D., Zhao, H., Liang, Z.: A texture feature analysis for diagnosis of pulmonary nodules using LIDC-IDRI database. In: IEEE International Conference on Medical Imaging Physics and Engineering, pp. 14–18 (2013)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding (2014). arXiv preprint arXiv:1408.5093
Jolliffe, I.: Principal Component Analysis. Wiley Online Library, Chichester (2005)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets (2014). arXiv preprint arXiv:1409.5185
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Prasanna, P., Tiwari, P., Madabhushi, A.: Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): distinguishing tumor confounders and molecular subtypes on MRI. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part III. LNCS, vol. 8675, pp. 73–80. Springer, Heidelberg (2014)
Roth, H.R., Lu, L., Seff, A., Cherry, K.M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., Summers, R.M.: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 520–527. Springer, Heidelberg (2014)
Suzuki, K., Li, F., Sone, S., Doi, K.: Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose ct by use of massive training artificial neural network. IEEE Trans. Med. Imaging 24(9), 1138–1150 (2005)
van der Walt, S., Schönberger, J.L., Nunez-Iglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T.: Scikit-image: Image processing in python. Technical report, PeerJ PrePrints (2014)
Way, T.W., Hadjiiski, L.M., Sahiner, B., Chan, H.P., Cascade, P.N., Kazerooni, E.A., Bogot, N., Zhou, C.: Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. Med. Phys. 33(7), 2323–2337 (2006)
Acknowledgments
The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. This paper is supported by the Chinese Academy of Sciences Key Deployment Program under Grant No. KGZD-EW-T03, the National Basic Research Program of China (973 Program) under Grant 2011CB707700, the National Natural Science Foundation of China under Grant No. 81227901, 61231004, 81370035, 81230030, 61301002, 61302025, major projects of Biomedicine Department of Shanghai Science and Technology Commission (13411950100), the Chinese Academy of Sciences Fellowship for Young International Scientists under Grant No. 2010Y2GA03, 2013Y1 GA0004, 2013Y1GB0005, the Chinese Academy of Sciences Visiting Professorship for Senior International Scientists under Grant No. 2012T1G0036, 2010T2G 36, 2012T1G0039, 2013T1G0013, the National High Technology Research and Development Program of China (863 Program) under 2012AA021105, the Guangdong Province-Chinese Academy of Sciences comprehensive strategic cooperation program under 2010A090100032 and 2012B090400039, the NSFC-NIH Biomedical collaborative research program under 81261120414, the National Science and Technology Supporting Plan under 2012BAI15B08, the Beijing Natural Science Foundation under Grant No. 4132080, the Fundamental Research Funds for the Central Universities under Grant No. 2013JBZ014.
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Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J. (2015). Multi-scale Convolutional Neural Networks for Lung Nodule Classification. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_46
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DOI: https://doi.org/10.1007/978-3-319-19992-4_46
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