Delineating 3D Angiogenic Sprouting in OCT Images via Multiple Active Contours

  • Ting Xu
  • Fengqiang Li
  • Duc-Huy T. Nguyen
  • Christopher S. Chen
  • Chao Zhou
  • Xiaolei Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8090)


Recent advances in Optical Coherence Tomography (OCT) has enabled high resolution imaging of three dimensional artificial vascular networks in vitro. Image segmentation can help quantify the morphological and topological properties of these curvilinear networks to facilitate quantitative study of the angiogenic process. Here we present a novel method to delineate the 3D artificial vascular networks imaged by spectral-domain OCT. Our method employs multiple Stretching Open Active Contours (SOACs) that evolve synergistically to retrieve both the morphology and topology of the underlying vascular networks. Quantification of the network properties can then be conducted based on the segmentation result. We demonstrate the potential of the proposed method by segmenting 3D artificial vasculature in simulated and real OCT images. We provide junction locations and vessel lengths as examples for quantifying angiogenic sprouting of 3D artificial vasculature from OCT images.


Angiogenesis Curvilinear Network Active Contours Optical Coherence Tomography 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nguyen, D.H.T., Stapleton, S.C., Yang, M.T., Cha, S.S., Choi, C.K., Galie, P.A., Chen, C.S.: Biomimetic model to reconstitute angiogenic sprouting morphogenesis in vitro. Proceedings of the National Academy of Sciences 110(17), 6712–6717 (2013)CrossRefGoogle Scholar
  2. 2.
    Huang, D., Swanson, E., Lin, C., Schuman, J., Stinson, W., Chang, W., Hee, M., Flotte, T., Gregory, K., Puliafito, C., et al.: Optical coherence tomography. Science 254(5035), 1178–1181 (1991)CrossRefGoogle Scholar
  3. 3.
    Blacher, S., Devy, L., Burbridge, M., Roland, G., Tucker, G., Noël, A., Foidart, J.M.: Improved quantification of angiogenesis in the rat aortic ring assay. Angiogenesis 4(2), 133–142 (2001)CrossRefGoogle Scholar
  4. 4.
    Niemisto, A., Dunmire, V., Yli-Harja, O., Zhang, W., Shmulevich, I.: Robust quantification of in vitro angiogenesis through image analysis. IEEE Transactions on Medical Imaging 24(4), 549–553 (2005)CrossRefGoogle Scholar
  5. 5.
    Abdul-Karim, M.A., Al-Kofahi, K., Brown, E.B., Jain, R.K., Roysam, B.: Automated tracing and change analysis of angiogenic vasculature from in vivo multiphoton confocal image time series. Microvascular Research 66(2), 113–125 (2003)CrossRefGoogle Scholar
  6. 6.
    Tyrrell, J.A., Mahadevan, V., Tong, R.T., Brown, E.B., Jain, R.K., Roysam, B.: A 2-d/3-d model-based method to quantify the complexity of microvasculature imaged by in vivo multiphoton microscopy. Microvascular Research 70(3), 165–178 (2005)CrossRefGoogle Scholar
  7. 7.
    Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3d vessel lumen segmentation techniques: Models, features and extraction schemes. Medical Image Analysis 13(6), 819–845 (2009)CrossRefGoogle Scholar
  8. 8.
    Çetingül, H.E., Gülsün, M.A., Tek, H.: A unified minimal path tracking and topology characterization approach for vascular analysis. In: Liao, H., "Eddie" Edwards, P.J., Pan, X., Fan, Y., Yang, G.-Z. (eds.) MIAR 2010. LNCS, vol. 6326, pp. 11–20. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1, 321–331 (1988)CrossRefGoogle Scholar
  10. 10.
    Li, H., Shen, T., Smith, M., Fujiwara, I., Vavylonis, D., Huang, X.: Automated actin filament segmentation, tracking and tip elongation measurements based on open active contour models. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, June 28-July 1 2009, pp. 1302–1305 (2009)Google Scholar
  11. 11.
    Nurgaliev, D., Gatanov, T., Needleman, D.J.: Chapter 25 - automated identification of microtubules in cellular electron tomography. In: Cassimeris, L., Tran, P. (eds.) Microtubules: in Vivo. Methods in Cell Biology, vol. 97, pp. 475–495. Academic Press (2010)Google Scholar
  12. 12.
    Smith, M.B., Li, H., Shen, T., Huang, X., Yusuf, E., Vavylonis, D.: Segmentation and tracking of cytoskeletal filaments using open active contours. Cytoskeleton 67(11), 693–705 (2010)CrossRefGoogle Scholar
  13. 13.
    Wang, Y., Narayanaswamy, A., Tsai, C.L., Roysam, B.: A broadly applicable 3-D neuron tracing method based on open-curve snake. Neuroinformatics 9, 193–217 (2011)CrossRefGoogle Scholar
  14. 14.
    Xu, T., Li, H., Shen, T., Ojkic, N., Vavylonis, D., Huang, X.: Extraction and analysis of actin networks based on open active contour models. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, March 30-April 2, pp. 1334–1340 (2011)Google Scholar
  15. 15.
    Chang, S., Kulikowski, C., Dunn, S., Levy, S.: Biomedical image skeletonization: A novel method applied to fibrin network structures. Studies in Health technology and Informatics 84(2), 901–905 (2001)Google Scholar
  16. 16.
    Lehmann, G.: Noise simulation. The Insight Journal (July 2010)Google Scholar
  17. 17.
    Narayanaswamy, A., Dwarakapuram, S., Bjornsson, C., Cutler, B., Shain, W., Roysam, B.: Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation. IEEE Transactions on Medical Imaging 29(3), 583–597 (2010)CrossRefGoogle Scholar
  18. 18.
    Leitgeb, R.A., Villiger, M., Bachmann, A.H., Steinmann, L., Lasser, T.: Extended focus depth for fourier domain optical coherence microscopy. Optics Letters 31(16), 2450–2452 (2006)CrossRefGoogle Scholar
  19. 19.
    Bolmont, T., Bouwens, A., Pache, C., Dimitrov, M., Berclaz, C., Villiger, M., Wegenast-Braun, B.M., Lasser, T., Fraering, P.C.: Label-free imaging of cerebral-amyloidosis with extended-focus optical coherence microscopy. The Journal of Neuroscience 32(42), 14548–14556 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ting Xu
    • 1
  • Fengqiang Li
    • 2
  • Duc-Huy T. Nguyen
    • 3
  • Christopher S. Chen
    • 3
    • 4
  • Chao Zhou
    • 2
  • Xiaolei Huang
    • 1
  1. 1.Department of Computer Science and EngineeringLehigh UniversityBethlehemUSA
  2. 2.Department of Electrical and Computer EngineeringLehigh UniversityBethlehemUSA
  3. 3.Department of Chemical and Biomolecular EngineeringUniversity of PennsylvaniaPhiladelphiaUSA
  4. 4.Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations