Semi-automatic classification of skeletal morphology in genetically altered mice using flat-panel volume computed tomography.
Semi-automatic classification of skeletal morphology in genetically altered mice using flat-panel volume computed tomography.
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DOI：
10.1371/journal.pgen.0030118
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发表时间：
2007-07
期刊：
影响因子：
4.5
通讯作者：
Alves, Frauke
中科院分区：
文献类型：
--
作者：
Dullin, Christian;Missbach-Guentner, Jeannine;Vogel, Wolfgang F.;Grabbe, Eckhardt;Alves, Frauke
Rapid progress in exploring the human and mouse genome has resulted in the generation of a multitude of mouse models to study gene functions in their biological context. However, effective screening methods that allow rapid noninvasive phenotyping of transgenic and knockout mice are still lacking. To identify murine models with bone alterations in vivo, we used flat-panel volume computed tomography (fpVCT) for high-resolution 3-D imaging and developed an algorithm with a computational intelligence system. First, we tested the accuracy and reliability of this approach by imaging discoidin domain receptor 2- (DDR2-) deficient mice, which display distinct skull abnormalities as shown by comparative landmark-based analysis. High-contrast fpVCT data of the skull with 200 μm isotropic resolution and 8-s scan time allowed segmentation and computation of significant shape features as well as visualization of morphological differences. The application of a trained artificial neuronal network to these datasets permitted a semi-automatic and highly accurate phenotype classification of DDR2-deficient compared to C57BL/6 wild-type mice. Even heterozygous DDR2 mice with only subtle phenotypic alterations were correctly determined by fpVCT imaging and identified as a new class. In addition, we successfully applied the algorithm to classify knockout mice lacking the DDR1 gene with no apparent skull deformities. Thus, this new method seems to be a potential tool to identify novel mouse phenotypes with skull changes from transgenic and knockout mice on the basis of random mutagenesis as well as from genetic models. However for this purpose, new neuronal networks have to be created and trained. In summary, the combination of fpVCT images with artificial neuronal networks provides a reliable, novel method for rapid, cost-effective, and noninvasive primary screening tool to detect skeletal phenotypes in mice. Transgenic mice are key models to shed new light on gene function during development and disease. Reliable high-throughput screening tools will facilitate the identification of transgenic mice with distinct phenotypes. In particular, alterations of the skull are difficult to detect by visual inspection due to its very complex morphological structure. Here, we used high-resolution flat-panel volume computed tomography (fpVCT), a novel semi-automatic screening tool to image skull-shape features of mice. The resulting 3-D datasets were combined with artificial neuronal networks and complex nonlinear computational models to permit rapid and automatic interpretation of the images. Compared to the extremely laborious landmark-based analysis, the manual work in our approach was reduced to the control of skull segmentation of images obtained by fpVCT. We applied our approach to genetically altered mice and various mouse strains and showed that it is an accurate and reliable method to successfully identify mice with skeletal phenotypes. We suggest the new methodology will also be a valuable tool for an in vivo, rapid, cost-effective, and reliable primary screen to identify skull abnormalities generated by random mouse mutagenesis experiments.
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影响因子：
2.2
作者：
Prescher, A;Meyers, A;von Keyserlingk, DG
通讯作者：
von Keyserlingk, DG
DOI：
10.1002/ajmg.10175
发表时间：
2002-02-01
期刊：
AMERICAN JOURNAL OF MEDICAL GENETICS
影响因子：
--
作者：
Richtsmeier, JT;Zumwalt, A;Reeves, RH
通讯作者：
Reeves, RH
影响因子：
4.5
作者：
Brown SD;Hancock JM;Gates H
通讯作者：
Gates H
影响因子：
4.6
作者：
Nieman, BJ;Flenniken, AM;Sled, JG
通讯作者：
Sled, JG
影响因子：
5.3
作者：
Vogel, WF;Aszódi, A;Pawson, T
通讯作者：
Pawson, T