Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics.

ABSTRACT: The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps betw

SUBMITTER: Thomas OO 

PROVIDER: S-EPMC9970057 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature