Spherical Harmonics based extraction and annotation of cell shape in 3D time-lapse microscopy sequences

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 a given homogeneous population. In this work, we propose a framework for cell shape extraction and classification for 3D time-lapse sequences of living cells, based on the SPherical HARMonics transform (SPHARM). Starting from an initial segmentation of the cell surface over time, this mathematical representation enables us to represent each extracted surface by a unique set of coefficients, while taking into account invariance properties such as translation or orientation. Then, unsupervised classification is conducted using a multi-class K-Means approach, so as to extract the most pertinent number of classes representing the different phases of the cell deformation. Experimental results on several sequences give encouraging results, and show that the proposed approach can be used to perform automated sequence annotation, and can be further applied to compare shape characteristics across different cell populations.