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Automatic Airway Segmentation in Chest CT Using Convolutional Neural Networks

  • Antonio Garcia-Uceda JuarezEmail author
  • H. A. W. M. Tiddens
  • M. de BruijneEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

Segmentation of the airway tree from chest computed tomography (CT) images is critical for quantitative assessment of airway diseases including bronchiectasis and chronic obstructive pulmonary disease (COPD). However, obtaining an accurate segmentation of airways from CT scans is difficult due to the high complexity of airway structures. Recently, deep convolutional neural networks (CNNs) have become the state-of-the-art for many segmentation tasks, and in particular the so-called Unet architecture for biomedical images. However, its application to the segmentation of airways still remains a challenging task. This work presents a simple but robust approach based on a 3D Unet to perform segmentation of airways from chest CTs. The method is trained on a dataset composed of 12 CTs, and tested on another 6 CTs. We evaluate the influence of different loss functions and data augmentation techniques, and reach an average dice coefficient of 0.8 between the ground-truth and our automated segmentations.

Keywords

Airway segmentation Convolutional neural networks Data augmentation Bronchiectasis CT 

Notes

Acknowledgments

This work has been funded by the EU Innovative Medicines Initiative (IMI). We would like to thank F. Dubost for his help with the experiments and with writing of this manuscript. We would also like to thank F. Calvet for sharing with us his implementation of elastic image deformation.

References

  1. 1.
    Lo, P., Sporring, J., Ashraf, H., Pedersen, J.H., de Bruijne, M.: Vessel-guided airway tree segmentation: a voxel classification approach. Med. Image Anal. 14(4), 527–538 (2010)CrossRefGoogle Scholar
  2. 2.
    Bauer, C., Bischof, H., Beichel, R.: Segmentation of airways based on gradient vector flow. In: Proceedings of 2nd International Workshop Pulmonary Image Analysis, pp. 191–201 (2009)Google Scholar
  3. 3.
    Liu, X., Chen, D.Z., Tawhai, M., Wu, X., Hoffman, E., Sonka, M.: Optimal graph search based segmentation of airway tree double surfaces across bifurcations. IEEE Trans. Med. Imag. 32, 493–510 (2012)CrossRefGoogle Scholar
  4. 4.
    Petersen, J., et al.: Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease. Med. Image Anal. 18, 531–541 (2014)CrossRefGoogle Scholar
  5. 5.
    Lo, P.: Extraction of airways from CT (EXACT09). IEEE Trans. Med. Imaging 31(11), 2093–2107 (2012)CrossRefGoogle Scholar
  6. 6.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)Google 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).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  8. 8.
    Meng, Q., Roth, H.R., Kitasaka, T., Oda, M., Ueno, J., Mori, K.: Tracking and segmentation of the airways in chest CT using a fully convolutional network. In: Medical Image Computing and Computer-Assisted Intervention MICCAI 2017, pp. 198–207 (2017)Google Scholar
  9. 9.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_49CrossRefGoogle Scholar
  10. 10.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 4th International Conference on 3D Vision (3DV) (2016)Google Scholar
  11. 11.
    Charbonnier, J.P., van Rikxoort, E.M., Setio, A.A.A., Schaefer-Prokop, C.M., van Ginneken, B., Ciompi, F.: Improving airway segmentation in computed tomography using leak detection with convolutional networks. Med. Image Anal. 36, 52–60 (2017)CrossRefGoogle Scholar
  12. 12.
    Selvan, R., Welling, M., Pedersen, J.H., Petersen, J., de Bruijne, M.: Mean field network based graph refinement with application to airway tree extraction. arXiv preprint arXiv:1804.03348 (2018)
  13. 13.
    Baumgartner, C.F., Koch, L.M., Pollefeys, M., Konukoglu, E.: An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation. In: Pop, M., Sermesant, M., Jodoin, P.-M., Lalande, A., Zhuang, X., Yang, G., Young, A., Bernard, O. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 111–119. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75541-0_12CrossRefGoogle Scholar
  14. 14.
    Kuo, W., et al.: Diagnosis of bronchiectasis and airway wall thickening in children with cystic fibrosis: objective airway-artery quantification. Eur. Radiol. 27(11), 4680–4689 (2017)CrossRefGoogle Scholar
  15. 15.
    Perez-Rovira, A., Kuo, W., Petersen, J., Tiddens, H.A.W.M., de Bruijne, M.: Automatic airway-artery analysis on lung CT to quantify airway wall thickening and bronchiectasis. Med. Phys. 43(10), 5736–5744 (2016)CrossRefGoogle Scholar
  16. 16.
    Krhenbhl, P., Koltun, V.: Efficient inference in Fully connected CRFs with Gaussian edge potentials. Adv. Neural Inf. Process. Syst. 24, 109–117 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and RadiologyErasmus MCRotterdamThe Netherlands
  2. 2.Department of Pediatric PulmonologyErasmus MC-Sophia Children HospitalRotterdamThe Netherlands
  3. 3.Department of Radiology and Nuclear MedicineErasmus MC-Sophia Children HospitalRotterdamThe Netherlands
  4. 4.Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark

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