Abstract
BackgroundHere we present an application of advanced registration and atlas building framework DRAMMS to the automated annotation of mouse mandibles through a series of tests using single and multi-atlas segmentation paradigms and compare the outcomes to the current gold standard, manual annotation.ResultsOur results showed multi-atlas annotation procedure yields landmark precisions within the human observer error range. The mean shape estimates from gold standard and multi-atlas annotation procedure were statistically indistinguishable for both Euclidean Distance Matrix Analysis (mean form matrix) and Generalized Procrustes Analysis (Goodall F-test). Further research needs to be done to validate the consistency of variance-covariance matrix estimates from both methods with larger sample sizes.ConclusionMulti-atlas annotation procedure shows promise as a framework to facilitate truly high-throughput phenomic analyses by channeling investigators efforts to annotate only a small portion of their datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12983-015-0127-8) contains supplementary material, which is available to authorized users.
Highlights
We present an application of advanced registration and atlas building framework DRAMMS to the automated annotation of mouse mandibles through a series of tests using single and multi-atlas segmentation paradigms and compare the outcomes to the current gold standard, manual annotation
The growing use of high resolution three-dimensional imaging, such as micro-computed tomography, along with advances in visualization and analytic software have provided researchers with the opportunity to study the morphology of organisms in more detail
Atlas sensitivity to initial sample To measure whether the initializing sample causes any bias in the outcome of the final atlas constructed, we tested for different outcomes by choosing unique initiating samples
Summary
We present an application of advanced registration and atlas building framework DRAMMS to the automated annotation of mouse mandibles through a series of tests using single and multi-atlas segmentation paradigms and compare the outcomes to the current gold standard, manual annotation. More and more researchers are turning to morphometric measurements obtained on 3D scans to quantitatively assess morphological differences in their experimental studies which might focus on effects of teratogens or mutations on craniofacial (CF) development, or study the normal CF development and variation [1,2,3,4,5,6,7,8,9] In this context, geometric morphometric methods (GMM) are a suite of Thanks to the increasing accessibility of microCT scanning in general and, tissue staining protocols for microCT in particular, it is possible to image dozens of adult mouse skulls or mandibles, or possibly hundreds of mice embryos in a single day.
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