Towards an Automatic Lung Cancer Screening System in Low Dose Computed Tomography

  • Guilherme ArestaEmail author
  • Teresa Araújo
  • Colin Jacobs
  • Bram van Ginneken
  • António Cunha
  • Isabel Ramos
  • Aurélio Campilho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)


We propose a deep learning-based pipeline that, given a low-dose computed tomography of a patient chest, recommends if a patient should be submitted to further lung cancer assessment. The algorithm is composed of a nodule detection block that uses the object detection framework YOLOv2, followed by a U-Net based segmentation. The found structures of interest are then characterized in terms of diameter and texture to produce a final referral recommendation according to the National Lung Screen Trial (NLST) criteria. Our method is trained using the public LUNA16 and LIDC-IDRI datasets and tested on an independent dataset composed of 500 scans from the Kaggle DSB 2017 challenge. The proposed system achieves a patient-wise recall of 89% while providing an explanation to the referral decision and thus may serve as a second opinion tool to speed-up and improve lung cancer screening.


Computer aided diagnosis Lung cancer Low dose computed tomography images Screening Deep learning 



Guilherme Aresta is funded by the FCT grant contract SFRH/BD/120435/2016. Teresa Araújo is funded by the FCT grant contract SFRH/BD/122365/2016. This study is associated with project NLST-375 and LNDetector, which is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness - COMPETE 2020 Programme and by the National Fundus through the Portuguese funding agency, FCT - Fundação para a Ciência e Tecnologia within project POCI-01-0145-FEDER-016673.


  1. 1.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2018. CA A Cancer J. Clin. 68(1), 7–30 (2018)Google Scholar
  2. 2.
    The National Lung Screening Trial Research Team: Reduced lung-cancer mortality with low-dose computed tomographic screening. New England J. Med. 365(5), 395–409 (2011)Google Scholar
  3. 3.
    Torre, L.A., Siegel, R.L., Ward, E.M., Jemal, A.: Global cancer incidence and mortality rates and trends-an update. Cancer Epidemiol. Biomark. Prev. 25(1), 16–27 (2016)CrossRefGoogle Scholar
  4. 4.
    Setio, A., Traverso, A., de Bel, T.: 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
  5. 5.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)CrossRefGoogle Scholar
  6. 6.
    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. 22(1), 48–62 (2015)CrossRefGoogle Scholar
  7. 7.
    Wang, S., Zhou, M., Liu, Z., et al.: Central focused convolutional neural networks: developing a data-driven model for lung nodule segmentation. Med. Image Anal. 40(3), 172–183 (2017)CrossRefGoogle Scholar
  8. 8.
    Armato, S.G., McLennan, G., Bidaut, L.: 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 (2011)CrossRefGoogle Scholar
  9. 9.
    Redmon, J., Farhadi, A.: YOLO9000: Better, faster, stronger. In: Proceedings of 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 6517–6525 (2017)Google Scholar
  10. 10.
    Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9. IEEE, 7–12 June 2015Google Scholar
  11. 11.
    Ding, J., Li, A., Hu, Z., Wang, L.: Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks, pp. 1–9. arXiv, June 2017CrossRefGoogle Scholar
  12. 12.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Guilherme Aresta
    • 1
    • 2
    Email author
  • Teresa Araújo
    • 1
    • 2
  • Colin Jacobs
    • 5
  • Bram van Ginneken
    • 5
  • António Cunha
    • 1
    • 3
  • Isabel Ramos
    • 4
  • Aurélio Campilho
    • 1
    • 2
  1. 1.INESC TECPortoPortugal
  2. 2.Faculty of Engineering of University of PortoPortoPortugal
  3. 3.University of Minho and Alto-DouroVila RealPortugal
  4. 4.Faculty of Medicine of University of PortoPortoPortugal
  5. 5.Radboud University Medical CenterNijmegenThe Netherlands

Personalised recommendations