Manifold Learning of COPD

  • Felix J. S. BragmanEmail author
  • Jamie R. McClelland
  • Joseph Jacob
  • John R. Hurst
  • David J. Hawkes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


Analysis of CT scans for studying Chronic Obstructive Pulmonary Disease (COPD) is generally limited to mean scores of disease extent. However, the evolution of local pulmonary damage may vary between patients with discordant effects on lung physiology. This limits the explanatory power of mean values in clinical studies. We present local disease and deformation distributions to address this limitation. The disease distribution aims to quantify two aspects of parenchymal damage: locally diffuse/dense disease and global homogeneity/heterogeneity. The deformation distribution links parenchymal damage to local volume change. These distributions are exploited to quantify inter-patient differences. We used manifold learning to model variations of these distributions in 743 patients from the COPDGene study. We applied manifold fusion to combine distinct aspects of COPD into a single model. We demonstrated the utility of the distributions by comparing associations between learned embeddings and measures of severity. We also illustrated the potential to identify trajectories of disease progression in a manifold space of COPD.



This work was supported by the EPSRC under Grant EP/H046410/1 and EP/K502959/1, and the UCLH NIHR RCF Senior Investigator Award under Grant RCF107/DH/2014. It used data (phs000179.v3.p2) from the COPDGene study, supported by NIH Grant U01HL089856 and U01HL089897.


  1. 1.
    Aljabar, P., Wolz, R., Srinivasan, L., Counsell, S.J., Rutherford, M.A., Edwards, A.D., Hajnal, J.V., Rueckert, D.: A combined manifold learning analysis of shape and appearance to characterize neonatal brain development. IEEE Trans. Med. Imaging 30(12), 2072–2086 (2011)CrossRefGoogle Scholar
  2. 2.
    Bragman, F.J.S., McClelland, J.R., Modat, M., Ourselin, S., Hurst, J.R., Hawkes, D.J.: Multi-scale analysis of imaging features and its use in the study of COPD exacerbation susceptible phenotypes. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 417–424. Springer, Cham (2014). doi: 10.1007/978-3-319-10443-0_53 CrossRefGoogle Scholar
  3. 3.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Galbán, C.J., Han, M.K., Boes, J.L., Chughtai, K.A., Charles, R., Johnson, T.D., Galbán, S., Rehemtulla, A., Kazerooni, E.A., Martinez, F.J., Ross, B.D.: CT-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat. Med. 18(11), 1711–1715 (2013)CrossRefGoogle Scholar
  5. 5.
    Harmouche, R., Ross, J.C., Diaz, A.A., Washko, G.R., Estepar, R.S.J.: A robust emphysema severity measure based on disease subtypes. Acad. Radiol. 23(4), 421–428 (2016)CrossRefGoogle Scholar
  6. 6.
    Heinrich, M.P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F.V., Brady, M., Schnabel, J.A.: MIND: modality independent neighbourhood descriptor for multi-modal deformable registration. Med. Image Anal. 16(7), 1423–1435 (2012)CrossRefGoogle Scholar
  7. 7.
    Levina, E., Bickel, P.: The earth mover’s distance is the Mallows distance: some insights from statistics. Eighth IEEE Int. Conf. Comput. Vis. 2, 251–256 (2001)CrossRefGoogle Scholar
  8. 8.
    Modat, M., McClelland, J., Ourselin, S.: Lung registration using the NiftyReg package. In: Medical Image Analysis for the Clinic: A Grand Challenge EMPIRE, vol. 10, pp. 33–42 (2010)Google Scholar
  9. 9.
    Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Comput. Methods Programs Biomed. 98(3), 278–284 (2010)CrossRefGoogle Scholar
  10. 10.
    Regan, E.A., Hokanson, J.E., Murphy, J.R., Make, B., Lynch, D.A., Beaty, T.H., Curran-Everett, D., Silverman, E.K., Crapo, J.D.: Genetic epidemiology of COPD (COPDGene) study design. COPD 7(1), 32–43 (2010)CrossRefGoogle Scholar
  11. 11.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)CrossRefzbMATHGoogle Scholar
  12. 12.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Felix J. S. Bragman
    • 1
    Email author
  • Jamie R. McClelland
    • 1
  • Joseph Jacob
    • 1
  • John R. Hurst
    • 2
  • David J. Hawkes
    • 1
  1. 1.Centre for Medical Image ComputingUniversity College LondonLondonUK
  2. 2.UCL RespiratoryUniversity College LondonLondonUK

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