Title
Signal Processing Challenges in Quantitative 3-D Cell Morphology: More than meets the eye
Abstract
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.
Year
DOI
Venue
2015
10.1109/MSP.2014.2359131
Signal Processing Magazine, IEEE  
Keywords
DocType
Volume
biomechanics,biomedical optical imaging,cellular biophysics,computer vision,data acquisition,deformation,image recognition,learning (artificial intelligence),medical image processing,microorganisms,optical microscopy,spatiotemporal phenomena,amoeboid cell deformation,bacterial invasion,cancer metastasis,computer vision,computerized approaches,image acquisition,immune responses,light microscopy,living cell morphology,machine-learning techniques,pattern recognition,quantitative 3D cell morphology,shape analysis,signal processing,spatiotemporal reproducibility,spatiotemporal resolution
Journal
32
Issue
ISSN
Citations 
1
1053-5888
3
PageRank 
References 
Authors
0.37
27
9