Geometrical characterization of fluorescently labelled surfaces from noisy 3D microscopy data

We present a fully automated algorithm to determine the location and curvatures of an object from 3D fluorescence images, such as those obtained using confocal or light‐sheet microscopy. The algorithm aims at reconstructing surface labelled objects with spherical topology and mild deformations from the spherical geometry with high accuracy, rather than reconstructing arbitrarily deformed objects with lower fidelity. Using both synthetic data with known geometrical characteristics and experimental data of spherical objects, we characterize the algorithm's accuracy over the range of conditions and parameters typically encountered in 3D fluorescence imaging. We show that the algorithm can detect the location of the surface and obtain a map of local mean curvatures with relative errors typically below 2% and 20%, respectively, even in the presence of substantial levels of noise. Finally, we apply this algorithm to analyse the shape and curvature map of fluorescently labelled oil droplets embedded within multicellular aggregates and deformed by cellular forces. Lay description Current fluorescence microscopy techniques allow the acquisition of three‐dimensional (3D) data of objects, such as living cells, synthetic vesicles, emulsion droplets, etc., for a wide range of studies in biology, physics and materials science. In many of these works, it is necessary to determine the geometry of the imaged object with sufficient accuracy so that quantitative studies can be performed. H...
Source: Journal of Microscopy - Category: Laboratory Medicine Authors: Tags: Original Article Source Type: research