Advertisement

Automatic Segmentation of Neurons from Fluorescent Microscopy Imaging

  • Silvia Baglietto
  • Ibolya E. Kepiro
  • Gerrit Hilgen
  • Evelyne Sernagor
  • Vittorio Murino
  • Diego Sona
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 881)

Abstract

Automatic detection and segmentation of neurons from microscopy acquisition is essential for statistically characterizing neuron morphology that can be related to their functional role. In this paper, we propose a combined pipeline that starts from the automatic detection of the soma through a new multiscale blob enhancement filtering. Then, a precise segmentation of the detected cell body is obtained by an active contour approach. The resulted segmentation is used as initial seed for the second part of the approach that proposes a dendrite arborization tracing method.

Notes

Acknowledgements

The research received financial support from the \(7^{th}\) Framework Programme for Research of the European Commision, Grant agreement no. 600847: RENVISION project of the Future and Emerging Technologies (FET) programme.

References

  1. 1.
    Baden, T., Berens, P., Franke, K., Rosón, M.R., Bethge, M., Euler, T.: The functional diversity of retinal ganglion cells in the mouse. Nature 529(7586), 345–350 (2016)CrossRefGoogle Scholar
  2. 2.
    Meijering, E.: Neuron tracing in perspective. Cytom. Part A 77(7), 693–704 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Basu, S., Aksel, A., Condron, B., Acton, S.T.: Tree2Tree: neuron segmentation for generation of neuronal morphology. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 548–551. IEEE (2010)Google Scholar
  4. 4.
    Longair, M.H., Baker, D.A., Armstrong, J.D.: Simple neurite tracer: open source software for reconstruction, visualization and analysis of neuronal processes. Bioinformatics 27(17), 2453–2454 (2011)CrossRefGoogle Scholar
  5. 5.
    Zheng, Z., Hong, P.: Incorporate deep-transfer-learning into automatic 3D neuron tracing. In: The First International Conference on Neuroscience and Cognitive Brain Information, BRAININFO 2016 (2016)Google Scholar
  6. 6.
    Chan, T.F., Vese, L., et al.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001)CrossRefGoogle Scholar
  7. 7.
    Yezzi, A., Tsai, A., Willsky, A.: A fully global approach to image segmentation via coupled curve evolution equations. J. Vis. Commun. Image Represent. 13(1), 195–216 (2002)CrossRefGoogle Scholar
  8. 8.
    Ge, Q., Li, C., Shao, W., Li, H.: A hybrid active contour model with structured feature for image segmentation. Signal Process. 108, 147–158 (2015)CrossRefGoogle Scholar
  9. 9.
    Wu, P., Yi, J., Zhao, G., Huang, Z., Qiu, B., Gao, D.: Active contour-based cell segmentation during freezing and its application in cryopreservation. IEEE Trans. Biomed. Eng. 62(1), 284–295 (2015)CrossRefGoogle Scholar
  10. 10.
    Lee, T.C., Kashyap, R.L., Chu, C.N.: Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP: Graph. Models Image Process. 56(6), 462–478 (1994)Google Scholar
  11. 11.
    Palágyi, K., Kuba, A.: A 3D 6-subiteration thinning algorithm for extracting medial lines. Pattern Recognit. Lett. 19(7), 613–627 (1998)CrossRefGoogle Scholar
  12. 12.
    Meijering, E.H., Jacob, M., Sarria, J.C.F., Unser, M.: A novel approach to neurite tracing in fluorescence microscopy images. In: SIP, pp. 491–495 (2003)Google Scholar
  13. 13.
    Benmansour, F., Cohen, L.D.: Tubular structure segmentation based on minimal path method and anisotropic enhancement. Int. J. Comput. Vis. 92(2), 192–210 (2011)CrossRefGoogle Scholar
  14. 14.
    Türetken, E., González, G., Blum, C., Fua, P.: Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors. Neuroinformatics 9(2–3), 279–302 (2011)CrossRefGoogle Scholar
  15. 15.
    Baglietto, S., Kepiro, I.E., Hilgen, G., Sernagor, E., Murino, V., Sona, D.: Segmentation of retinal ganglion cells from fluorescent microscopy imaging. In: BIOSTEC, pp. 17–23 (2017)Google Scholar
  16. 16.
    Gulyanon, S., Sharifai, N., Kim, M.D., Chiba, A., Tsechpenakis, G.: CRF formulation of active contour population for efficient three-dimensional neurite tracing. In: 2016 IEEE 13th International Symposium on Biomedical Imaging, ISBI, pp. 593–597. IEEE (2016)Google Scholar
  17. 17.
    Lankton, S., Tannenbaum, A.: Localizing region-based active contours. IEEE Trans. Image Process. 17(11), 2029–2039 (2008)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998).  https://doi.org/10.1007/BFb0056195CrossRefGoogle Scholar
  19. 19.
    Liu, J., White, J.M., Summers, R.M.: Automated detection of blob structures by Hessian analysis and object scale. In: 2010 17th IEEE International Conference on Image Processing, ICIP, pp. 841–844. IEEE (2010)Google Scholar
  20. 20.
    Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. In: International Workshop on Image Processing, Real-Time Edge and Motion Detection (1979)Google Scholar
  21. 21.
    Lathen, G., Jonasson, J., Borga, M.: Phase based level set segmentation of blood vessels. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008)Google Scholar
  22. 22.
    Läthén, G., Jonasson, J., Borga, M.: Blood vessel segmentation using multi-scale quadrature filtering. Pattern Recognit. Lett. 31(8), 762–767 (2010)CrossRefGoogle Scholar
  23. 23.
    Zijdenbos, A.P., Dawant, B.M., Margolin, R., Palmer, A.C., et al.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imag. 13(4), 716–724 (1994)CrossRefGoogle Scholar
  24. 24.
    Mukherjee, S., Condron, B., Acton, S.T.: Tubularity flow field—A technique for automatic neuron segmentation. IEEE Trans. Image Process. 24(1), 374–389 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Silvia Baglietto
    • 1
    • 2
  • Ibolya E. Kepiro
    • 3
  • Gerrit Hilgen
    • 4
  • Evelyne Sernagor
    • 4
  • Vittorio Murino
    • 1
    • 5
  • Diego Sona
    • 1
    • 6
  1. 1.Pattern Analysis and Computer VisionIstituto Italiano di TecnologiaGenoaItaly
  2. 2.Department of Naval, Electric, Electronic and Telecommunication EngineeringUniversity of GenovaGenoaItaly
  3. 3.NanophysicsIstituto Italiano di TecnologiaGenoaItaly
  4. 4.Institute of Neuroscience, NewcastleNewcastle-upon-TyneUK
  5. 5.Department of Computer ScienceUniversity of VeronaVeronaItaly
  6. 6.NeuroInformatics LaboratoryFondazione Bruno KesslerTrentoItaly

Personalised recommendations