The Effect of Automated Landmark Identification on Morphometric Analyses Using the Collaborative Cross

Morphometric analysis of anatomical landmarks allows researchers to identify specific differences in craniofacial shape between natural populations or experimental groups, but manually identifying landmarks is time-consuming. Previous studies have shown that image registration based automated landmark identification may adequately replace manual landmarking in certain situations. We compare manually and automatically generated adult mouse skull landmarks and subsequent morphometric analyses to elucidate how switching from manual to automated landmarking will impact the morphometric analysis results for large mouse (Mus musculus) samples (n=1206) that represent wide “normal” phenotypic variation (54 genotypes). This large sample size allowed us to investigate how landmarking method impacts estimated differences in genotype mean shape, estimates of shape variance, and whether there were any genotype-specific biases in landmark identification. In addition, we tested whether an initial registration of specimen images to genotype specific averages improves automatic landmark identification accuracy. Our results indicated that automated landmark placement was significantly different than manual landmark placement, but that all methods identified similar patterns of skull shape covariation across our large sample. The addition of a preliminary genotype-specific registration step as part of a two-level procedure did not improve on the accuracy of one-level automatic landmark placement. The landmarks with the lowest automatic landmark accuracy are found in locations with poor image registration alignment. The most serious outliers within morphometric analysis of automated landmarks displayed instances of stochastic image registration error that are likely representative of errors common when applying image registration methods to µCT datasets that were initially collected with manual landmarking in mind. Additional efforts during specimen preparation and image acquisition can help reduce the number of registration errors and improve registration results. Previously identified tendencies of atlas-based registration methods to underestimate variation may reduce statistical power for tests designed to identify differences in variation or covariation patterns. For appropriate samples and research questions, our image registration based automated landmarking method can eliminate the time required for manual landmarking and identify similar general patterns of mean skull shape differences across a large genetically variable mouse sample.