Advertisement

Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentation

  • Aurélien Lucchi
  • Carlos Becker
  • Pablo Márquez Neila
  • Pascal Fua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

In this paper, we improve upon earlier approaches to segmenting mitochondria in Electron Microscopy images by explicitly modeling the double membrane that encloses mitochondria, as well as using features that capture context over an extended neighborhood. We demonstrate that this results in both improved classification accuracy and reduced computational requirements for training.

References

  1. 1.
    Campello, S., Scorrano, L.: Mitochondrial Shape Changes: Orchestrating Cell Pathophysiology. EMBO Reports 11(9), 678–684 (2010)CrossRefGoogle Scholar
  2. 2.
    Lee, D., Lee, K., Ho, W., Lee, S.: Target Cell-Specific Involvement of Presynaptic Mitochondria in Post-Tetanic Potentiation at Hippocampal Mossy Fiber Synapses. The Journal of NeuroScience 27(50), 13603–13613 (2007)CrossRefGoogle Scholar
  3. 3.
    Knott, A., Perkins, G., Schwarzenbacher, R., Bossy-Wetzel, E.: Mitochondrial Fragmentation in Neurodegeneration. Nature Reviews. Neuroscience 9(7), 505–518 (2008)CrossRefGoogle Scholar
  4. 4.
    Poole, A., Thomas, R., Andrews, L., Mcbride, H., Whitworth, A., Pallanck, L.: The Pink1/parkin Pathway Regulates Mitochondrial Morphology. Proceedings of the National Academy of Sciences of the United States of America 105(5), 1638–1643 (2008)CrossRefGoogle Scholar
  5. 5.
    Campbell, N., Williamson, B., Heyden, R.: Biology: Exploring Life. Pearson Prentice Hall (2006)Google Scholar
  6. 6.
    Vitaladevuni, S., Mishchenko, Y., Genkin, A., Chklovskii, D., Harris, K.: Mitochondria Detection in Electron Microscopy Images. In: Workshop on Microscopic Image Analysis with Applications in Biology (2008)Google Scholar
  7. 7.
    Narasimha, R., Ouyang, H., Gray, A., McLaughlin, S., Subramaniam, S.: Automatic Joint Classification and Segmentation of Whole Cell 3D Images. PR 42, 1067–1079 (2009)Google Scholar
  8. 8.
    Sommer, C., Straehle, C., Koethe, U., Hamprecht, F.: Interactive Learning and Segmentation Tool Kit. In: Systems Biology of Human Disease, pp. 230–233 (2010)Google Scholar
  9. 9.
    Lucchi, A., Smith, K., Achanta, R., Knott, G., Fua, P.: Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features. TMI 31(2), 474–486 (2011)Google Scholar
  10. 10.
    Kumar, R., Vazquez-Reina, A., Pfister, H.: Radon-Like Features and Their Application to Connectomics. In: Workshop on MMBIA (2010)Google Scholar
  11. 11.
    Kreshuk, A., Straehle, C.N., Sommer, C., Koethe, U., Knott, G., Hamprecht, F.: Automated Segmentation of Synapses in 3D EM Data. In: ISBI (2011)Google Scholar
  12. 12.
    Lucchi, A., Li, Y., Smith, K., Fua, P.: Structured Image Segmentation Using Kernelized Features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 400–413. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Lucchi, A., Li, Y., Fua, P.: Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets. In: CVPR (June 2013)Google Scholar
  14. 14.
    Delong, A., Boykov, Y.: Globally Optimal Segmentation of Multi-Region Objects. In: ICCV, pp. 285–292 (2009)Google Scholar
  15. 15.
    Becker, C., Ali, K., Knott, G., Fua, P.: Learning Context Cues for Synapse Segmentation. TMI 32(10), 1864–1877 (2013)Google Scholar
  16. 16.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Suesstrunk, S.: SLIC Superpixels Compared to State-Of-The-Art Superpixel Methods. PAMI 34(11), 2274–2281 (2012)CrossRefGoogle Scholar
  17. 17.
    Lafferty, J., Mccallum, A., Pereira, F.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: ICML (2001)Google Scholar
  18. 18.
    Smith, K., Carleton, A., Lepetit, V.: Fast Ray Features for Learning Irregular Shapes. In: ICCV, pp. 397–404 (2009)Google Scholar
  19. 19.
    Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The Pascal Visual Object Classes Challenge (VOC 2010) Results (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Aurélien Lucchi
    • 2
  • Carlos Becker
    • 1
  • Pablo Márquez Neila
    • 1
  • Pascal Fua
    • 1
  1. 1.Computer Vision LaboratoryEPFLLausanneSwitzerland
  2. 2.Department of Computer ScienceETHZZürichSwitzerland

Personalised recommendations