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Abstract
Dense surface registration, commonly used in computer science, could aid the biological sciences in accurate and comprehensive quantification of biological phenotypes. However, few toolboxes exist that are openly available, non-expert friendly, and validated in a way relevant to biologists. Here, we report a customizable toolbox for reproducible high-throughput dense phenotyping of 3D images, specifically geared towards biological use. Given a target image, a template is first oriented, repositioned, and scaled to the target during a scaled rigid registration step, then transformed further to fit the specific shape of the target using a non-rigid transformation. As validation, we use n = 41 3D facial images to demonstrate that the MeshMonk registration is accurate, with 1.26 mm average error, across 19 landmarks, between placements from manual observers and using the MeshMonk toolbox. We also report no variation in landmark position or centroid size significantly attributable to landmarking method used. Though validated using 19 landmarks, the MeshMonk toolbox produces a dense mesh of vertices across the entire surface, thus facilitating more comprehensive investigations of 3D shape variation. This expansion opens up exciting avenues of study in assessing biological shapes to better understand their phenotypic variation, genetic and developmental underpinnings, and evolutionary history.
Highlights
The phenotypic complement to genomics is phenomics, which aims to obtain high-throughput and high-dimensional phenotyping in line with our ability to characterize genomes[1]
Our goal is to contribute to a transition of 3D image registration technology from computer science to biology by providing a customizable framework that is available to the public and geared towards biological studies of morphology
We introduce the MeshMonk toolbox, which implements a variation of iterative closest point (ICP) incorporating a regularized non-rigid registration deformation, is informed by extensive tests of various algorithmic options[44], and can be applied to 3D facial images as well as 3D scans of other complex morphological structures
Summary
The phenotypic complement to genomics is phenomics, which aims to obtain high-throughput and high-dimensional phenotyping in line with our ability to characterize genomes[1]. Computer scientists are consistently developing and improving algorithms for the identification of landmark points on 3D image data[10,16,17,25,26,27,28,29,30,31,32,33,34], but fewer of these developments have been utilized in biological studies of morphology[18,35,36,37,38,39,40,41,42,43], and more rare is quantifying morphology on a dense scale[10,16,17,18,34,37]. We validate the MeshMonk toolbox using a sparse set of landmarks on the human face, our intention is for the toolbox to facilitate morphometric analysis of the entire shape surface, not just sparse landmark positions
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