Advertisement

Phenotype Detection in Morphological Mutant Mice Using Deformation Features

  • Sharmili Roy
  • Xi Liang
  • Asanobu Kitamoto
  • Masaru Tamura
  • Toshihiko Shiroishi
  • Michael S. Brown
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8151)

Abstract

Large-scale global efforts are underway to knockout each of the approximately 25,000 mouse genes and interpret their roles in shaping the mammalian embryo. Given the tremendous amount of data generated by imaging mutated prenatal mice, high-throughput image analysis systems are inevitable to characterize mammalian development and diseases. Current state-of-the-art computational systems offer only differential volumetric analysis of pre-defined anatomical structures between various gene-knockout mice strains. For subtle anatomical phenotypes, embryo phenotyping still relies on the laborious histological techniques that are clearly unsuitable in such big data environment. This paper presents a system that automatically detects known phenotypes and assists in discovering novel phenotypes in μCT images of mutant mice. Deformation features obtained from non-linear registration of mutant embryo to a normal consensus average image are extracted and analyzed to compute phenotypic and candidate phenotypic areas. The presented system is evaluated using C57BL/10 embryo images. All cases of ventricular septum defect and polydactyly, well-known to be present in this strain, are successfully detected. The system predicts potential phenotypic areas in the liver that are under active histological evaluation for possible phenotype of this mouse line.

Keywords

Mutant Mouse Defect Detection Ventricular Septum Defect Jacobian Determinant Detection Rule 
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.

References

  1. 1.
    Collins, F.S., Rossant, J., Wurst, W.: A mouse for all reasons. Cell 128, 9–13 (2007)CrossRefGoogle Scholar
  2. 2.
    Mouse Genome Sequencing Consortium: Initial Sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002)Google Scholar
  3. 3.
    Zamyadi, M., Baghdadi, L., Lerch, J.P., Bhattacharya, S., Schneider, J.E., Henkelman, R.M.: Mouse embryonic phenotyping by morphometric analysis of MR images. Physiol. Genomics 42A, 89–95 (2010)Google Scholar
  4. 4.
    Wong, M.D., Dorr, A.E., Walls, J.R., Lerch, J.P., Henkelman, R.M.: A novel 3D mouse embryo atlas based on micro-CT. Development 139(17), 3248–3256 (2012)CrossRefGoogle Scholar
  5. 5.
    Cleary, J.O., Modat, M., Norris, F.C., Price, A.N., Jayakody, S.A., Martinez-Barbera, J.P., Greene, N.D.E., Hawkes, D.J., Ordidge, R.J., Scambler, P.J., Ourselin, S., Lythgoe, M.F.: Magnetic resonance virtual histology for embryos: 3D atlases for automated high-throughput phenotyping. Neuroimage 54(2), 769–778 (2011)CrossRefGoogle Scholar
  6. 6.
    Nieman, B.J., Wong, M.D., Henkelman, R.M.: Genes into geometry: imaging for mouse development in 3D. Curr. Opin. Genet. Dev. 21(5), 638–646 (2011)CrossRefGoogle Scholar
  7. 7.
    Norris, F.C., Modat, M., Cleary, J.O., Price, A.N., McCue, K., Scambler, P.J., Ourselin, S., Lythgoe, M.F.: Segmentation propagation using a 3D embryo atlas for high-throughput MRI phenotyping: comparison and validation with manual segmentation. Magn. Reson. Med. 69(3), 877–883 (2013)CrossRefGoogle Scholar
  8. 8.
    Degenhardt, K., Wright, A.C., Horng, D., Padmanabhan, A., Epstein, J.A.: Rapid 3D phenotyping of cardiovascular development in mouse embryos by micro-CT with iodine staining. Circ. Cardiovascular Imaging 3(3), 314–322 (2010)CrossRefGoogle Scholar
  9. 9.
    Cleary, J.O., Price, A.N., Thomas, D.L., Scambler, P.J., Kyriakopoulou, V., McCue, K., Schneider, J.E., Ordidge, R.J., Lythgoe, M.F.: Cardiac phenotyping in ex vivo murine embryos using μMRI. NMR Biomed. 22(8), 857–866 (2009)CrossRefGoogle Scholar
  10. 10.
    Xie, Z., Yang, D., Stephenson, D., Morton, D., Hicks, C., Brown, T., Bocan, T.: Characterizing the regional structural difference of the brain between tau transgenic (rTg4510) and Wild-Type Mice using MRI. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 308–315. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Nieman, B.J., Flenniken, A.M., Adamson, S.L., Henkelman, R.M., Sled, J.G.: Anatomical phenotyping in the brain and skull of a mutant mouse by magnetic resonance imaging and computed tomography. Physiol. Genomics 24(2), 154–162 (2006)CrossRefGoogle Scholar
  12. 12.
    Mattes, D., Haynor, D.R., Vesselle, H., Lewellyn, T.K., Eubank, W.: Nonrigid multimodality image registration. In: Proc. SPIE, vol. 4322, pp. 1609–1619 (2001)Google Scholar
  13. 13.
    Staring, M., Klein, S., Pluim, J.P.W.: A rigidty penalty term for nonrigid registration. Med. Phys. 34(11), 4098–4108 (2007)CrossRefGoogle Scholar
  14. 14.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.: elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sharmili Roy
    • 1
  • Xi Liang
    • 2
  • Asanobu Kitamoto
    • 2
  • Masaru Tamura
    • 3
  • Toshihiko Shiroishi
    • 3
  • Michael S. Brown
    • 1
  1. 1.School of ComputingNational University of SingaporeSingapore
  2. 2.National ICT Australia (NICTA)Australia
  3. 3.National Institute of GeneticsJapan

Personalised recommendations