Monitoring of adherent live cells morphology using the undecimated wavelet transform multivariate image analysis (UWT‐MIA)

Abstract

Cell morphology is an important macroscopic indicator of cellular physiology and is increasingly used as a mean of probing culture state in vitro. Phase contrast microscopy (PCM) is a valuable tool for observing live cells morphology over long periods of time with minimal culture artifact. Two general approaches are commonly used to analyze images: individual object segmentation and characterization by pattern recognition. Single-cell segmentation is difficult to achieve in PCM images of adherent cells since their contour is often irregular and blurry, and the cells bundle together when the culture reaches confluence. Alternatively, pattern recognition approaches such as the undecimated wavelet transform multivariate image analysis (UWT-MIA), allow extracting textural features from PCM images that are correlated with cellular morphology. A partial least squares (PLS) regression model built using textural features from a set of 200 ground truth images was shown to predict the distribution of cellular morphological features (major and minor axes length, orientation, and roundness) with good accuracy for most images. The PLS models were then applied on a large dataset of 631,136 images collected from live myoblast cell cultures acquired under different conditions and grown in two different culture media. The method was found sensitive to morphological changes due to cell growth (culture time) and those introduced by the use of different culture media, and was able to distinguish both sources of variations. The proposed approach is promising for application on large datasets of PCM live-cell images to assess cellular morphology and growth kinetics in real-time which could be beneficial for high-throughput screening as well as automated cell culture kinetics assessment and control applications. Biotechnol. Bioeng. 2017;114: 141-153. © 2016 Wiley Periodicals, Inc.

Dynamics of Endothelial Cell Responses to Laminar Shear Stress on Surfaces Functionalized with Fibronectin-Derived Peptides

“…The cell confluency (surface coverage) and cell alignment in time-lapse phase contrast images were quantified by undecimated wavelet transform multivariate image analysis (UWT-MIA) as previously described. , The undecimated wavelet transform performs space-frequency decomposition of the 2D image signals by convoluting the signals with a dilated mother wavelet at different scales and along different orientations within the image. The result of the image convolution is a so-called wavelet detail coefficients matrix (wavelet plane) representing how well the wavelet matches the signal at a given scale and position in the image.…”

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“…Third, cell or nucleus segmentation methods are mainly based on related theories [28,29], such as wavelet analysis [30,31], mathematical morphology [32,33,34], genetic algorithm [35] and neural networks [36,37,38].…”

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“…Aside from training, the process does not require any further specifications or interventions and is applicable to any given dataset [59]. Another example is the Undecimated Wavelet Transform Multivariate Image Analysis (UWT-MIA) [60], a pattern recognition tool, which can extract textual features from phase contrast images and also extracts shape descriptors such as major and minor axes length, orientation, and roundness. UWT-MIA is an analysis tool, which can simultaneously analyze data relevant for scale and orientation of the cells, which proves to be advantageous at high cell densities [60].…”

“…Another example is the Undecimated Wavelet Transform Multivariate Image Analysis (UWT-MIA) [60], a pattern recognition tool, which can extract textual features from phase contrast images and also extracts shape descriptors such as major and minor axes length, orientation, and roundness. UWT-MIA is an analysis tool, which can simultaneously analyze data relevant for scale and orientation of the cells, which proves to be advantageous at high cell densities [60]. Another approach for addressing the problem of recognizing cells within densely packed groups is the multi-resolution analysis and maximum-likelihood (MAMLE) [61].…”