Signal Processing in Quantitative 3D Cell Morphology

Modern developments in light microscopy have allowed the observation of cell deformation with remarkable spatiotemporal resolution and reproducibility. Analyzing such phenomena is of particular interest for the signal processing and computer vision communities due to the numerous computational challenges involved, from image acquisition all the way to shape analysis and pattern recognition and interpretation.

This article aims at providing an up-to-date overview of the problems, solutions, and remaining challenges in deciphering the morphology of living cells via computerized approaches, with a particular focus on shape description frameworks and their exploitation using machine-learning techniques. As a concrete illustration, we use our recently acquired data on amoeboid cell deformation, motivated by its direct implication in immune responses, bacterial invasion, and cancer metastasis.