High-Throughput Glomeruli Analysis of \(\mu \)CT Kidney Images Using Tree Priors and Scalable Sparse Computation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

Kidney-related diseases have incrementally become one major cause of death. Glomeruli are the physiological units in the kidney responsible for the blood filtration. Therefore, their statistics including number and volume, directly describe the efficiency and health state of the kidney. Stereology is the current quantification method relying on histological sectioning, sampling and further 2D analysis, being laborious and sample destructive. New micro-Computed Tomography (\(\mu \)CT) imaging protocols resolute structures down to capillary level. However large-scale glomeruli analysis remains challenging due to object identifiability, allotted memory resources and computational time. We present a methodology for high-throughput glomeruli analysis that incorporates physiological apriori information relating the kidney vasculature with estimates of glomeruli counts. We propose an effective sampling strategy that exploits scalable sparse segmentation of kidney regions for refined estimates of both glomeruli count and volume. We evaluated the proposed approach on a database of \(\mu \)CT datasets yielding a comparable segmentation accuracy as an exhaustive supervised learning method. Furthermore we show the ability of the proposed sampling strategy to result in improved estimates of glomeruli counts and volume without requiring a exhaustive segmentation of the \(\mu \)CT image. This approach can potentially be applied to analogous organizations, such as for example the quantification of alveoli in lungs.

Notes

Acknowledgements

This work is funded by the Kommission für Technologie und Innovation (KTI) Project No. 14055.1 PFIW-IW.

References

  1. 1.
    Baumann, P., et al.: Sparse-reduced computation - enabling mining of massively-large data sets. In: Proceedings of ICPRAM 2016, pp. 224–231 (2016)Google Scholar
  2. 2.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Bruce, M., et al.: Berne and Levy physiology, 6th edn. Elsevier (2010)Google Scholar
  4. 4.
    Cullen-McEwen, L., Drago, J., Bertram, J.: Nephron endowment in glial cell line-derived neurotrophic factor (GDNF) heterozygous mice. Kidney Int. 60(1), 31–36 (2001)CrossRefGoogle Scholar
  5. 5.
    Cullen-McEwen, L., et al.: Nephron number, renal function, and arterial pressure in aged GDNF heterozygous mice. Hypertension 41(2), 335–40 (2003)CrossRefGoogle Scholar
  6. 6.
    Davison, A., Hinkley, D.: Bootstrap Methods and their Applications. Cambridge University Press, Cambridge (1997)CrossRefMATHGoogle Scholar
  7. 7.
    Hochbaum, D.: Polynomial time algorithms for ratio regions and a variant of normalized cut. IEEE Trans. Pattern Anal. Mach. Intel. 32, 889–898 (2010)Google Scholar
  8. 8.
    Kerschnitzki, M., et al.: Architecture of the osteocyte network correlates with bone material quality. J. Bone Miner. Res. 28(8), 1837–1845 (2013)CrossRefGoogle Scholar
  9. 9.
    Murray, C.: The physiological principle of minimum work: II. Oxygen exchange in capillaries. Proc. National Acad. Sci. United States Am. 12(5), 299–304 (1926)Google Scholar
  10. 10.
    Murray, C.: The physiological principle of minimum work: I. the vascular system and the cost of blood volume. Proc. Natl. Acad. Sci. U.S.A. 12(3), 207–214 (1926)CrossRefGoogle Scholar
  11. 11.
    Rempfler, M., et al.: Reconstructing cerebrovascular networks under local physiological constraints by integer programming. Med. Image Anal. 25(1), 86–94 (2015). special Issue on MICCAI 2014Google Scholar
  12. 12.
    Schneider, M., et al.: Tissue metabolism driven arterial tree generation. Med. Image Anal. 16(7), 1397–1414 (2012). special Issue on MICCAI 2011Google Scholar
  13. 13.
    Sherman, T.: On connecting large vessels to small. the meaning of murray’s law. J. Gen. Physiol. 78(4), 431–453 (1981)CrossRefGoogle Scholar
  14. 14.
    Thompson, S.: Sampling. Wiley series in probability and statistics. Wiley (2002)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.Institute of AnatomyUniversity of BernBernSwitzerland
  3. 3.Department of Business AdministrationUniversity of BernBernSwitzerland

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