End-to-End Lung Nodule Detection in Computed Tomography

  • Dufan Wu
  • Kyungsang Kim
  • Bin Dong
  • Georges El Fakhri
  • Quanzheng LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)


Computer aided diagnostic (CAD) system is crucial for modern medical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Computer vision can capture features that is subtle to human observers, so it is desirable to design a CAD system operating on the raw data. In this paper, we proposed a deep-neural-network-based detection system for lung nodule detection in computed tomography (CT). A primal-dual-type deep reconstruction network was applied first to convert the raw data to the image space, followed by a 3-dimensional convolutional neural network (3D-CNN) for the nodule detection. For efficient network training, the deep reconstruction network and the CNN detector was trained sequentially first, then followed by one epoch of end-to-end fine tuning. The method was evaluated on the Lung Image Database Consortium image collection (LIDC-IDRI) with simulated forward projections. With 144 multi-slice fanbeam projections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data. It also demonstrated superior detection performance compared to detectors trained on the reconstructed images. The proposed method is general and could be expanded to most detection tasks in medical imaging.


Computer aided diagnosis Artificial neural networks Computed tomography 


  1. 1.
    Kalra, M.K., Maher, M.M., Toth, T.L., et al.: Strategies for CT radiation dose optimization. Radiology 230(3), 619–628 (2004)CrossRefGoogle Scholar
  2. 2.
    Greenspan, H., van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)CrossRefGoogle Scholar
  3. 3.
    Adler J. and Oktem O. Learned primal-dual reconstruction. arXiv preprint, arXiv:1707.06474 (2017)
  4. 4.
    Sun, J., Li, H., Xu, Z., et al.: Deep ADMM-net for compressive sensing MRI. Adv. Neural Inf. Process. Syst. 29, 10–18 (2016)Google Scholar
  5. 5.
    Bojarski M., Testa D. D., Dworakowski D., et al. End to end learning for self-driving cars. arXiv preprint, arXiv:1604.07316 (2016)
  6. 6.
    Graves, A., Jaitly, T.: Towards end-to-end speech recognition with recurrent neural networks. In: Proceedings of the 31st International Conference on Machine Learning (ICML-2014), pp. 1764–1772. PMLR, Beijing, China (2014)Google Scholar
  7. 7.
    Armato, S.G., 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
  8. 8.
    De Wit, J., Hammack, D.: 2nd place solution for the 2017 national datasicence bowl. Accessed 1 Mar 2018
  9. 9.
    Setio, A.A.A., Traverso, A., de Bel, T., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Med. Image Anal. 42, 1–13 (2017)CrossRefGoogle Scholar
  10. 10.
    Zhu, W., Liu, C., Fan, W. and Xie, X., Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification. arXiv preprint arXiv:1801.09555 (2017)

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Dufan Wu
    • 1
  • Kyungsang Kim
    • 1
  • Bin Dong
    • 2
  • Georges El Fakhri
    • 1
  • Quanzheng Li
    • 1
    Email author
  1. 1.Gordon Center for Medical ImagingMassachusetts General Hospital and Harvard Medical SchoolBostonUSA
  2. 2.Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina

Personalised recommendations