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)


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.


Mutant Mouse Defect Detection Ventricular Septum Defect Jacobian Determinant Detection Rule 
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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

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