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Automatic Artery-Vein Separation from Thoracic CT Images Using Integer Programming

  • Christian Payer
  • Michael Pienn
  • Zoltán Bálint
  • Andrea Olschewski
  • Horst Olschewski
  • Martin Urschler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9350)

Abstract

Automated computer-aided analysis of lung vessels has shown to yield promising results for non-invasive diagnosis of lung diseases. In order to detect vascular changes affecting arteries and veins differently, an algorithm capable of identifying these two compartments is needed. We propose a fully automatic algorithm that separates arteries and veins in thoracic computed tomography (CT) images based on two integer programs. The first extracts multiple subtrees inside a graph of vessel paths. The second labels each tree as either artery or vein by maximizing both, the contact surface in their Voronoi diagram, and a measure based on closeness to accompanying bronchi. We evaluate the performance of our automatic algorithm on 10 manual segmentations of arterial and venous trees from patients with and without pulmonary vascular disease, achieving an average voxel based overlap of 94.1% (range: 85.0% – 98.7%), outperforming a recent state-of-the-art interactive method.

Keywords

Voronoi Diagram Separation Algorithm Thoracic Compute Tomography Vessel Segmentation Venous Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Murphy, K., van Ginneken, B., Schilham, A.M.R., de Hoop, B.J., Gietema, H.A., Prokop, M.: A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med. Image Anal. 13(5), 757–770 (2009)CrossRefGoogle Scholar
  2. 2.
    Masutani, Y., MacMahon, H., Doi, K.: Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis. IEEE Trans. Med. Imaging 21(12), 1517–1523 (2002)CrossRefGoogle Scholar
  3. 3.
    Linguraru, M.G., Pura, J.A., Van Uitert, R.L., Mukherjee, N., Summers, R.M., Minniti, C., Gladwin, M.T., Kato, G., Machado, R.F., Wood, B.J.: Segmentation and quantification of pulmonary artery for noninvasive CT assessment of sickle cell secondary pulmonary hypertension. Med. Phys. 37(4), 1522–1532 (2010)CrossRefGoogle Scholar
  4. 4.
    Estépar, R.S.J., Kinney, G.L., Black-Shinn, J.L., Bowler, R.P., Kindlmann, G.L., Ross, J.C., Kikinis, R., Han, M.K., Come, C.E., Diaz, A.A., Cho, M.H., Hersh, C.P., Schroeder, J.D., Reilly, J.J., Lynch, D.A., Crapo, J.D., Wells, J.M., Dransfield, M.T., Hokanson, J.E., Washko, G.R.: Computed tomographic measures of pulmonary vascular morphology in smokers and their clinical implications. Am. J. Respir. Crit. Care Med. 188(2), 231–239 (2013)CrossRefGoogle Scholar
  5. 5.
    Helmberger, M., Pienn, M., Urschler, M., Kullnig, P., Stollberger, R., Kovacs, G., Olschewski, A., Olschewski, H., Bálint, Z.: Quantification of tortuosity and fractal dimension of the lung vessels in pulmonary hypertension patients. PLoS One 9(1), e87515 (2014)Google Scholar
  6. 6.
    van Rikxoort, E.M., van Ginneken, B.: Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review. Phys. Med. Biol. 58, R187–R220 (2013)Google Scholar
  7. 7.
    Bülow, T., Wiemker, R., Blaffert, T., Lorenz, C., Renisch, S.: Automatic extraction of the pulmonary artery tree from multi-slice CT data. In: Proc. SPIE 5746, Med. Imaging Physiol. Funct. Struct. from Med. Images, pp. 730–740 (2005)Google Scholar
  8. 8.
    Park, S., Lee, S.M., Kim, N., Seo, J.B., Shin, H.: Automatic reconstruction of the arterial and venous trees on volumetric chest CT. Med. Phys. 40(7), 071906 (2013)Google Scholar
  9. 9.
    Saha, P.K., Gao, Z., Alford, S.K., Sonka, M., Hoffman, E.A.: Topomorphologic separation of fused isointensity objects via multiscale opening: Separating arteries and veins in 3-D pulmonary CT. IEEE Trans. Med. Imaging 29(3), 840–851 (2010)CrossRefGoogle Scholar
  10. 10.
    Gao, Z., Grout, R.W., Holtze, C., Hoffman, E.A., Saha, P.K.: A new paradigm of interactive artery/vein separation in noncontrast pulmonary CT imaging using multiscale topomorphologic opening. IEEE Trans. Biomed. Eng. 59(11), 3016–3027 (2012)CrossRefGoogle Scholar
  11. 11.
    Kitamura, Y., Li, Y., Ito, W., Ishikawa, H.: Adaptive higher-order submodular potentials for pulmonary artery-vein segmentation. In: Proc. Fifth Int. Work. Pulm. Image Anal., pp. 53–61 (2013)Google Scholar
  12. 12.
    Benmansour, F., Türetken, E., Fua, P.: Tubular Geodesics using Oriented Flux: An ITK Implementation. The Insight Journal (2013)Google Scholar
  13. 13.
    Türetken, E., Benmansour, F., Andres, B., Pfister, H., Fua, P.: Reconstructing loopy curvilinear structures using integer programming. In: IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1822–1829 (2013)Google Scholar
  14. 14.
    Robben, D., Türetken, E., Sunaert, S., Thijs, V., Wilms, G., Fua, P., Maes, F., Suetens, P.: Simultaneous segmentation and anatomical labeling of the cerebral vasculature. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 307–314. Springer, Heidelberg (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Christian Payer
    • 1
    • 2
  • Michael Pienn
    • 2
  • Zoltán Bálint
    • 2
  • Andrea Olschewski
    • 2
  • Horst Olschewski
    • 3
  • Martin Urschler
    • 4
    • 1
  1. 1.Institute for Computer Graphics and Vision, BioTechMedGraz University of TechnologyGrazAustria
  2. 2.Ludwig Boltzmann Institute for Lung Vascular ResearchGrazAustria
  3. 3.Department of PulmonologyMedical University of GrazGrazAustria
  4. 4.Ludwig Boltzmann Institute for Clinical Forensic ImagingGrazAustria

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