PDE-constrained shape registration to characterize biological growth and morphogenesis from imaging data
Aishwarya Pawar, Linlin Li, Arun K Gosain, David M Umulis, Adrian B, Tepole

TL;DR
This paper introduces a PDE-constrained shape registration method that models biological tissue growth and deformation from imaging data, enabling better analysis of morphogenetic processes.
Contribution
It extends previous work by incorporating growth and elastic contributions into a PDE-based shape registration framework for biological tissues.
Findings
Successfully applied to zebrafish embryo epiboly
Analyzed tissue expansion during skin reconstruction
Enhanced understanding of tissue deformation processes
Abstract
We propose a PDE-constrained shape registration algorithm that captures the deformation and growth of biological tissue from imaging data. Shape registration is the process of evaluating optimum alignment between pairs of geometries through a spatial transformation function. We start from our previously reported work, which uses 3D tensor product B-spline basis functions to interpolate 3D space. Here, the movement of the B-spline control points, composed with an implicit function describing the shape of the tissue, yields the total deformation gradient field. The deformation gradient is then split into growth and elastic contributions. The growth tensor captures addition of mass, i.e. growth, and evolves according to a constitutive equation which is usually a function of the elastic deformation. Stress is generated in the material due to the elastic component of the deformation alone.…
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Taxonomy
TopicsCell Image Analysis Techniques · 3D Shape Modeling and Analysis · Cellular Mechanics and Interactions
