Abstract
Understanding the mechanisms involved in cell deformation and motility is of major interest in numerous areas of life sciences. Precise quantification of cell shape requires robust shape description tools to be amenable to subsequent analysis and classification. The main difficulty lies in the great variability of cell shapes within so-called ”homogeneous” populations. While basic shape descriptors fail to provide sufficient information and lack robustness to small shape variabilities, here we investigate the use of the Spherical Harmonics transform to efficiently extract and quantify cell shape and deformation. Using real 3D+t biological imaging data sets, we show that this tool allows to precisely characterize the cell shape both in a static and a dynamic manner, allowing to extract a wide range of qualitative and quantitative parameters, such as outliers individuals, redundant shape configurations and spatiotemporal deformation patterns.
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