Joint Motion Estimation and Source Identification Using Convective Regularisation with an Application to the Analysis of Laser Nanoablations

Lukas F. Lang (), Nilankur Dutta (), Elena Scarpa (), Bénédicte Sanson (), Carola-Bibiane Schönlieb () and Jocelyn Étienne ()
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Lukas F. Lang: University of Cambridge, Department of Applied Mathematics and Theoretical Physics
Nilankur Dutta: Université Grenoble Alpes, Laboratoire Interdisciplinaire de Physique
Elena Scarpa: University of Cambridge, Department of Physiology, Development and Neuroscience
Bénédicte Sanson: University of Cambridge, Department of Physiology, Development and Neuroscience
Carola-Bibiane Schönlieb: University of Cambridge, Department of Applied Mathematics and Theoretical Physics
Jocelyn Étienne: Université Grenoble Alpes, Laboratoire Interdisciplinaire de Physique

A chapter in Time-dependent Problems in Imaging and Parameter Identification, 2021, pp 191-227 from Springer

Abstract: Abstract We propose a variational method for joint motion estimation and source identification in one-dimensional image sequences. The problem is motivated by fluorescence microscopy data of laser nanoablations of cell membranes in live Drosophila embryos, which can be conveniently—and without loss of significant information—represented in space-time plots, so called kymographs. Based on mechanical models of tissue formation, we propose a variational formulation that is based on the nonhomogenous continuity equation and investigate the solution of this ill-posed inverse problem using convective regularisation. We show existence of a minimiser of the minimisation problem, derive the associated Euler–Lagrange equations, and numerically solve them using a finite element discretisation together with Newton’s method. Based on synthetic data, we demonstrate that source estimation can be crucial whenever signal variations can not be explained by advection alone. Furthermore, we perform an extensive evaluation and comparison of various models, including standard optical flow, based on manually annotated kymographs that measure velocities of visible features. Finally, we present results for data generated by a mechanical model of tissue formation and demonstrate that our approach reliably estimates both a velocity and a source.

Date: 2021
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DOI: 10.1007/978-3-030-57784-1_7

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