Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

  • Jia Ding
  • Aoxue Li
  • Zhiqiang Hu
  • Liwei WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


Early detection of pulmonary cancer is the most promising way to enhance a patient’s chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection (average FROC-score of 0.893, ranking the 1st place over all submitted results), which outperforms the best result on the leaderboard of the LUNA16 Challenge (average FROC-score of 0.864).



This work was partially supported by National Basic Research Program of China (973 Program) (grant no. 2015CB352502), NSFC (61573026) and the MOE-Microsoft Key Laboratory of Statistics and Machine Learning, Peking University. We would like to thank the anonymous reviewers for their valuable comments on our paper.


  1. 1.
    Alberts, D.S.: The national lung screening trial research team reduced lung-cancer mortality with low-dose computed tomographic screening. New Engl. J. Med. 365(5), 395–409 (2011)CrossRefGoogle Scholar
  2. 2.
    Armato, S., McLennan, G., Bidaut, L., 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)CrossRefGoogle Scholar
  3. 3.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)Google Scholar
  4. 4.
    Lin, M., Chen, Q., Yan, S.: Network in network (2013). arXiv:1312.4400
  5. 5.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  6. 6.
    Setio, A.A.A., Ciompi, F., Litjens, G., et al.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35(5), 1160–1169 (2016)CrossRefGoogle Scholar
  7. 7.
    Setio, A.A.A., Traverso, A., Bel, T., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge (2016). arXiv:1612.08012
  8. 8.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2015. CA Cancer J. Clin. 65(1), 5–29 (2015)CrossRefGoogle Scholar
  9. 9.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations, pp. 1–9 (2015)Google Scholar
  10. 10.
    Torres, E.L., Fiorina, E., Pennazio, F., et al.: Large scale validation of the M5L lung CAD on heterogeneous CT datasets. Med. Phys. 42(4), 1477–1489 (2015)CrossRefGoogle Scholar
  11. 11.
    Zagoruyko, S., Komodakis, N.: Wide residual networks (2016). arXiv:1605.07146

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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