Automated and Adaptable Quantification of Cellular Alignment from Microscopic Images for Tissue Engineering Applications

Abstract

Cellular alignment plays a critical role in functional, physical, and biological characteristics of many tissue types, such as muscle, tendon, nerve, and cornea. Current efforts toward regeneration of these tissues include replicating the cellular microenvironment by developing biomaterials that facilitate cellular alignment. To assess the functional effectiveness of the engineered microenvironments, one essential criterion is quantification of cellular alignment. Therefore, there is a need for rapid, accurate, and adaptable methodologies to quantify cellular alignment for tissue engineering applications. To address this need, we developed an automated method, binarization-based extraction of alignment score (BEAS), to determine cell orientation distribution in a wide variety of microscopic images. This method combines a sequenced application of median and band-pass filters, locally adaptive thresholding approaches and image processing techniques. Cellular alignment score is obtained by applying a robust scoring algorithm to the orientation distribution. We validated the BEAS method by comparing the results with the existing approaches reported in literature (i.e., manual, radial fast Fourier transform-radial sum, and gradient based approaches). Validation results indicated that the BEAS method resulted in statistically comparable alignment scores with the manual method (coefficient of determination R 2 = 0.92). Therefore, the BEAS method introduced in this study could enable accurate, convenient, and adaptable evaluation of engineered tissue constructs and biomaterials in terms of cellular alignment and organization.

Extracting Cell and Fibre Orientation from Microscopic Images:Computational Methods and Tools

“…Organization of cells within a tissue dictates the anisotropy of its ECM in the spatial setup and together they characterize the tissue and the organs that they make up 13 . Alignment of tissue cells plays a key role in various important functions of the tissues such as neuron regeneration, defining mechanical and physical properties of tissue, pattern formation in embryogenesis, tissue maturation, etc.…”

“…Many other approaches have been tested, mainly, Huang 32 (measures fuzziness and Shanon entropy), Li 33 (minimizing cross-entropy), max entropy (ME) 34 (uses entropy of histogram), Otsu 35 (minimizes weighted sum of variance of two classes), Shanbhag (SH) 36 (entropybased approach), Yen 37 (discrepancy between thresholded image and original image as well as the size of the same are considered here) and binarization-based extraction of alignment score (BEAS) 13 (uses mean and standard deviation for local thresholding). For defining the threshold, these approaches have exploited different features of the image as mentioned.…”

Image segmentation of mesenchymal stem cells in diverse culturing conditions

“…In [6], behaviors of the fibroblast cells are studied and mitotic events are tracked which appeared as bright spots in their images. However, gradient based techniques are not known for performing well on low contrast and noisy images [27]. Further, study in [6] does not explicitly describe the image diversity in its dataset.…”

“…In a similar BEAS approach [27], Xu et al study the cellular orientations and show that adaptive thresholding is effective in segmenting a diverse set of cell images. After applying the preprocessing steps to the cell images, they use the local thresholding approach of Savoula and Pietikäinen [22] for segmentation.…”

“…A number of traditional threshold-based image segmentation approaches fail to achieve satisfying results on our datasets. The best performing approach, the recent binarizationbased extraction of alignment score (BEAS) [27] approach, uses an adaptive threshold based segmentation. Its threshold is estimated based on the mean and standard deviation, which cannot cover sufficient statistics of a cell image distribution.…”