2015
DOI: 10.1002/term.1994
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An insight into morphometric descriptors of cell shape that pertain to regenerative medicine

Abstract: Cellular morphology has recently been indicated as a powerful indicator of cellular function. The analysis of cell shape has evolved from rudimentary forms of microscopic visual inspection to more advanced methodologies that utilize high-resolution microscopy coupled with sophisticated computer hardware and software for data analysis. Despite this progress, there is still a lack of standardization in quantification of morphometric parameters. In addition, uncertainty remains as to which methodologies and param… Show more

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Cited by 40 publications
(36 citation statements)
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“…For image analysis, single randomly chosen cells were contoured and shape parameters (area, perimeter, circularity, and convexity) were obtained using NIH Image J software [38] .…”
Section: Methodsmentioning
confidence: 99%
“…For image analysis, single randomly chosen cells were contoured and shape parameters (area, perimeter, circularity, and convexity) were obtained using NIH Image J software [38] .…”
Section: Methodsmentioning
confidence: 99%
“…The well-known Kudo's pit-pattern classification is the result of combing endoscopic findings with histologic findings of glandular crypts (Kudo, Hirota, Nakajima, et al, 1994). New techniques and parameters are continually defining and redefining morphological categories (Lobo, See, Biggs, & Pandit, 2016). Complex branching shapes can be better classified with the help of new parameters such as the chord intersection ratio, convexity ratio, and maximum concave area ratio.…”
Section: Discussionmentioning
confidence: 99%
“…The choice of descriptors can be purely heuristic (that is, by observing the cells), or educated with the help of data analysis techniques such as dimensionality reduction algorithms (e.g., NMF, principal component analysis [PCA], t-distributed stochastic neighbor embedding [t-SNE], or uniform manifold approximation and projection [UMAP]), which simplify visualization on the basis of statistical or topological principles. 102 , 103 An extensive list of descriptors is available, 104 , 105 starting from very simple features (e.g., area, elongation) and covering a broad range of applications; for example, the ramification factor and branching points are adapted to approximately count filopodia and neuron dendrites, whereas principal axes have been used to quantify the relative orientation of dividing neuroblasts 106 and growing bacteria. 107 …”
Section: Detecting Characterizing and Following Cells In Microscopymentioning
confidence: 99%