Sara M. Rolfe

The paper introduces a semi-automated pipeline to quantify craniofacial asymmetry in fetal mouse imaging, identifying gene knockout strains with significant asymmetry.

This paper introduces a 3D morphological simulation approach to test phenotype detection methods, showing that detectability depends on effect size, sample size, and ROI selection.

This study evaluates a semi-automated tool to quantify craniofacial variations in zebrafish models of human skeletal diseases, finding phenotypic differences for four genes and parallels to human symptoms for two.

This paper introduces MEMOS, a deep learning tool for segmenting 50 anatomical structures in mouse embryos, validated against atlas-based methods and knockout strain data.

This paper uses computational anatomy to quantify 3D craniofacial phenotypes in zebrafish mutants, identifying shape differences in bmp1a mutants.