Comparison of Conventional and Deep Learning Based Methods for Pulmonary Nodule Segmentation in CT Images

  • Joana RochaEmail author
  • António Cunha
  • Ana Maria Mendonça
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11804)


Lung cancer is among the deadliest diseases in the world. The detection and characterization of pulmonary nodules are crucial for an accurate diagnosis, which is of vital importance to increase the patients’ survival rates. The segmentation process contributes to the mentioned characterization, but faces several challenges, due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper proposes two methods for pulmonary nodule segmentation in Computed Tomography (CT) scans. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the center of the nodule, and consequently the filter’s support points, matching the initial border coordinates. This preliminary segmentation is then refined to include mainly the nodular area, and no other regions (e.g. vessels and pleural wall). The second approach is based on Deep Learning, using the U-Net to achieve the same goal. This work compares both performances, and consequently identifies which one is the most promising tool to promote early lung cancer screening and improve nodule characterization. Both methodologies used 2653 nodules from the LIDC database: the SBF based one achieved a Dice score of 0.663, while the U-Net achieved 0.830, yielding more similar results to the ground truth reference annotated by specialists, and thus being a more reliable approach.


Computer-aided diagnosis Conventional Deep Learning Lung Nodule Segmentation Sliding Band Filter U-Net 



This work is financed by National Funds through the Portuguese funding agency, FCT – Fundação para a Ciência e a Tecnologia within project: UID/EEA/50014/2019.


  1. 1.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deepconvolutional encoder-decoder architecture for imagesegmentation, November 2015. arXiv:1511.00561 [cs].
  2. 2.
    Dashtbozorg, B., Mendonça, A.M., Campilho, A.: Optic disc segmentation using the sliding band filter. Comput. Biol. Med. 56, 1–12 (2015). Scholar
  3. 3.
    Jiang, F., et al.: Medical image semantic segmentation based on deep learning. Neural Comput. Appl. 29(5), 1257–1265 (2018). Scholar
  4. 4.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017). Scholar
  5. 5.
    Pereira, C.S., Mendonça, A.M., Campilho, A.: Evaluation of contrast enhancement filters for lung nodule detection. In: Kamel, M., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 878–888. Springer, Heidelberg (2007). Scholar
  6. 6.
    Quelhas, P., Marcuzzo, M., Mendonca, A.M., Campilho, A.: Cell nuclei and cytoplasm joint segmentation using the sliding band filter. IEEE Trans. Med. Imaging 29(8), 1463–1473 (2010). Scholar
  7. 7.
    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
  8. 8.
    Roth, H.R., et al.: Deep learning and its application to medical image segmentation, March 2018. arXiv:1803.08691 [cs].
  9. 9.
    Shakibapour, E., Cunha, A., Aresta, G., Mendonça, A.M., Campilho, A.: An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung CT scans. Exp. Syst. Appl. 119, 415–428 (2019)CrossRefGoogle Scholar
  10. 10.
    Torre, L.A., Siegel, R.L., Jemal, A.: Lung cancer statistics. In: Ahmad, A., Gadgeel, S. (eds.) Lung Cancer and Personalized Medicine. AEMB, vol. 893, pp. 1–19. Springer, Cham (2016). Scholar
  11. 11.
    Wang, S., et al.: Central focused convolutional neural networks: developing a data-driven model for lung nodule segmentation. Med. Image Anal. 40, 172–183 (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Joana Rocha
    • 1
    • 2
    Email author
  • António Cunha
    • 2
    • 3
  • Ana Maria Mendonça
    • 1
    • 2
  1. 1.Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  2. 2.INESC TEC – INESC Technology and SciencePortoPortugal
  3. 3.Universidade de Trás-os-Montes e Alto DouroVila RealPortugal

Personalised recommendations