Advertisement

Pathological Pulmonary Lobe Segmentation from CT Images Using Progressive Holistically Nested Neural Networks and Random Walker

  • Kevin George
  • Adam P. Harrison
  • Dakai Jin
  • Ziyue Xu
  • Daniel J. Mollura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)

Abstract

Automatic pathological pulmonary lobe segmentation(PPLS) enables regional analyses of lung disease, a clinically important capability. Due to often incomplete lobe boundaries, PPLS is difficult even for experts, and most prior art requires inference from contextual information. To address this, we propose a novel PPLS method that couples deep learning with the random walker (RW) algorithm. We first employ the recent progressive holistically-nested network (P-HNN) model to identify potential lobar boundaries, then generate final segmentations using a RW that is seeded and weighted by the P-HNN output. We are the first to apply deep learning to PPLS. The advantages are independence from prior airway/vessel segmentations, increased robustness in diseased lungs, and methodological simplicity that does not sacrifice accuracy. Our method posts a high mean Jaccard score of \(0.888\pm 0.164\) on a held-out set of 154 CT scans from lung-disease patients, while also significantly (\(p < 0.001\)) outperforming a state-of-the-art method.

Keywords

Lung lobe segmentation CT Holistically nested neural network Fissure Random walker 

References

  1. 1.
    Doel, T., Gavaghan, D.J., Grau, V.: Review of automatic pulmonary lobe segmentation methods from CT. Comput. Med. Imaging Graph. 40, 13–29 (2015)CrossRefGoogle Scholar
  2. 2.
    Raasch, B.N., Carsky, E.W., Lane, E.J., O’Callaghan, J.P., Heitzman, E.R.: Radiographic anatomy of the interlobar fissures: a study of 100 specimens. Am. J. Roentgenol. 138(6), 1043–1049 (1982)CrossRefGoogle Scholar
  3. 3.
    Wang, J., Betke, M., Ko, J.P.: Pulmonary fissure segmentation on CT. Med. Image Anal. 10(4), 530–547 (2006)CrossRefGoogle Scholar
  4. 4.
    Cronin, P., Gross, B.H., Kelly, A.M., Patel, S., Kazerooni, E.A., Carlos, R.C.: Normal and accessory fissures of the lung: evaluation with contiguous volumetric thin-section multidetector CT. Eur. J. Radiol. 75(2), e1–e8 (2010)CrossRefGoogle Scholar
  5. 5.
    Bragman, F., McClelland, J., Jacob, J., Hurst, J., Hawkes, D.: Pulmonary lobe segmentation with probabilistic segmentation of the fissures and a groupwise fissure prior. IEEE Trans. Med. Imaging 36(8), 1650–1663 (2017)CrossRefGoogle Scholar
  6. 6.
    Doel, T., Matin, T.N., Gleeson, F.V., Gavaghan, D.J., Grau, V.: Pulmonary lobe segmentation from CT images using fissureness, airways, vessels and multilevel b-splines. In: 2012 9th IEEE Internationl Symposium on Biomedical Imaging (ISBI), pp. 1491–1494, May 2012Google Scholar
  7. 7.
    Ross, J.C., San José Estépar, R., Kindlmann, G., Díaz, A., Westin, C.-F., Silverman, E.K., Washko, G.R.: Automatic lung lobe segmentation using particles, thin plate splines, and maximum a posteriori estimation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 163–171. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15711-0_21 CrossRefGoogle Scholar
  8. 8.
    Pu, J., Zheng, B., Leader, J.K., Fuhrman, C., Knollmann, F., Klym, A., Gur, D.: Pulmonary lobe segmentation in CT examinations using implicit surface fitting. IEEE Trans. Med. Imaging 28(12), 1986–1996 (2009)CrossRefGoogle Scholar
  9. 9.
    Harrison, A.P., Xu, Z., George, K., Lu, L., Summers, R.M., Mollura, D.J.: Progressive and multi-path holistically nested neural networks for pathological lung segmentation from CT images. In: MICCAI 2017, Proceedings (2017)Google Scholar
  10. 10.
    Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  11. 11.
    Karwoski, R.A., Bartholmai, B., Zavaletta, V.A., Holmes, D., Robb, R.A.: Processing of CT images for analysis of diffuse lung disease in the lung tissue research consortium. In: Proceedings of SPIE 6916, Medical Imaging 2008: Physiology, Function, and Structure from Medical Images (2008)Google Scholar
  12. 12.
    Xie, S., Tu, Z.: Holistically-nested edge detection. In: The IEEE International Conference on Computer Vision (ICCV), December 2015Google Scholar
  13. 13.
    Roth, H.R., Lu, L., Farag, A., Sohn, A., Summers, R.M.: Spatial aggregation of holistically-nested networks for automated pancreas segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 451–459. Springer, Cham (2016). doi: 10.1007/978-3-319-46723-8_52 CrossRefGoogle Scholar
  14. 14.
    Zhou, Y., Xie, L., Shen, W., Fishman, E., Yuille, A.: Pancreas segmentation in abdominal CT scan: a coarse-to-fine approach. CoRR/abs/1612.08230 (2016)Google Scholar
  15. 15.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale visual recognition. In: ICLR (2015)Google Scholar
  16. 16.
    Lassen, B., van Rikxoort, E.M.: Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures, vessels, and bronchi. IEEE Trans. Med. Imaging 32(2), 210–222 (2013)CrossRefGoogle Scholar
  17. 17.
    van Rikxoort, E.M., Prokop, M., de Hoop, B., Viergever, M.A., Pluim, J.P.W., van Ginneken, B.: Automatic segmentation of pulmonary lobes robust against incomplete fissures. IEEE Trans. Med. Imaging 29(6), 1286–1296 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG (outside the US) 2017

Authors and Affiliations

  • Kevin George
    • 1
  • Adam P. Harrison
    • 1
  • Dakai Jin
    • 1
  • Ziyue Xu
    • 1
  • Daniel J. Mollura
    • 1
  1. 1.National Institutes of HealthBethesdaUSA

Personalised recommendations