DOI: 10.5061/dryad.bvq83bkcr
title: PyTorch geometric datasets for morphVQ models

description: 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 between pairs of specimens in a dataset, we can analyze
 and explore shape variation. morphVQ uses Consistent ZoomOut refinement to
 improve these functional maps and produce a new representation of shape
 variation and area-based and conformal (angular) latent shape space
 differences (LSSDs). We compare this new representation of shape variation
 to shape variables obtained via manual digitization and auto3DGM, an
 existing approach to automated morphological phenotyping. We find that
 LSSDs compare favorably to modern 3DGM and auto3DGM while being more
 computationally efficient. By characterizing whole surfaces, our method
 incorporates more morphological detail in shape analysis. We can classify
 known biological groupings, such as Genus affiliation with comparable
 accuracy. The shape spaces produced by our method are similar to those
 produced by modern 3DGM and to auto3DGM, and distinctiveness functions
 derived from LSSDs show us how shape variation differs between groups.
 morphVQ can capture shape in an automated fashion while avoiding the
 limitations of manually digitized landmarks and thus represents a novel
 and computationally efficient addition to the geometric morphometrics
 toolkit.
description: The main dataset consists of 102 triangular meshes from laser
 surface scans of hominoid cuboid bones. These cuboids were from
 wild-collected individuals housed in the American Museum of Natural
 History, the National Museum of Natural History, the Harvard Museum of
 Comparative Biology, and the Field Museum. Hylobates, Pongo, Gorilla, Pan,
 and Homo are all well represented. Each triangular mesh
 is denoised, remeshed, and cleaned using the Geomagic Studio Wrap
 Software. The resulting meshes vary in vertex-count/resolution from 2,000
 - 390,000. Each mesh is then upsampled or decimated to an even 12,000
 vertices using the recursive subdivisions process and quadric decimation
 algorithm implemented in VTK python. The first of the
 two smaller datasets is comprised of 26 hominoid medial cuneiforms meshes
 isolated from laser surface scans obtained from the same museum
 collections listed above. The second dataset comprises 33 mouse humeri
 meshes from micro-CT data (34.5 μm resolution using a Skyscan 1172). These
 datasets were processed identically to the 102 hominoid cuboid meshes
 introduced above.
description: These datasets are customized Torch Geometric Datasets that
 contain raw .off polygon meshes as well as preprocessed .pt files needed
 for training morphVQ models. morphVQ can be found at https://github.com/oothomas/morphVQ.