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Since genetically modified mice have become more common in biomedical research as models of human disease, a need has also grown for efficient and quantitative methods to assess mouse phenotype. One powerful means of phenotyping is characterization of anatomy in mutant vs. normal populations. Anatomical phenotyping requires visualization of structures in situ, quantification of complex shape differences between mouse populations, and detection of subtle or diffuse abnormalities during high-throughput survey work. These aims can be achieved with imaging techniques adapted from clinical radiology, such as magnetic resonance imaging and computed tomography. These imaging technologies provide an excellent nondestructive method for visualization of anatomy in live individuals or specimens. The computer-based analysis of these images then allows thorough anatomical characterizations. We present an automated method for analyzing multiple-image data sets. This method uses image registration to identify corresponding anatomy between control and mutant groups. Within- and between-group shape differences are used to map regions of significantly differing anatomy. These regions are highlighted and represented quantitatively by displacements and volume changes. This methodology is demonstrated for a partially characterized mouse mutation generated by N-ethyl-N-nitrosourea mutagenesis that is a putative model of the human syndrome oculodentodigital dysplasia, caused by point mutations in the gene encoding connexin 43.
Nieman et al. (Thu,) studied this question.