Simple and accurate methods for quantifying deformation, disruption, and development in biological tissues
Author(s) -
John J. Boyle,
Maiko Kume,
Matthew A. Wyczalkowski,
Larry A. Taber,
Robert Pless,
Younan Xia,
Guy M. Genin,
Stavros Thomopoulos
Publication year - 2014
Publication title -
journal of the royal society interface
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 139
eISSN - 1742-5689
pISSN - 1742-5662
DOI - 10.1098/rsif.2014.0685
Subject(s) - deformation (meteorology) , computer science , strain (injury) , tracking (education) , simple (philosophy) , identification (biology) , biological system , displacement (psychology) , function (biology) , algorithm , materials science , biology , pedagogy , philosophy , botany , epistemology , psychology , anatomy , psychotherapist , evolutionary biology , composite material
When mechanical factors underlie growth, development, disease or healing, they often function through local regions of tissue where deformation is highly concentrated. Current optical techniques to estimate deformation can lack precision and accuracy in such regions due to challenges in distinguishing a region of concentrated deformation from an error in displacement tracking. Here, we present a simple and general technique for improving the accuracy and precision of strain estimation and an associated technique for distinguishing a concentrated deformation from a tracking error. The strain estimation technique improves accuracy relative to other state-of-the-art algorithms by directly estimating strain fields without first estimating displacements, resulting in a very simple method and low computational cost. The technique for identifying local elevation of strain enables for the first time the successful identification of the onset and consequences of local strain concentrating features such as cracks and tears in a highly strained tissue. We apply these new techniques to demonstrate a novel hypothesis in prenatal wound healing. More generally, the analytical methods we have developed provide a simple tool for quantifying the appearance and magnitude of localized deformation from a series of digital images across a broad range of disciplines.
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