Response in shape and size of individual p31 cancer cells to cisplatin and ouabain: a computerized image analysis of cell halo characteristics during continuous perfusion

Behnam‐Motlagh, Parviz; Grankvist, Kjell; Henriksson, Roger; Engström, Karl Gunnar

doi:10.1002/1097-0320(20000701)40:3<198::aid-cyto4>3.0.co;2-1

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…The halo artefact, which is accentuated by phase contrast techniques, is known to interfere with further image processing, but may also simplify the detection of cell walls by enhancing boundaries between the targeted specimen and medium . Segmentation of the halo and cell may be achieved using an intensity threshold method . The underlying assumption of the intensity threshold method is that cells have significantly and consistently different intensities than the background.…”

Section: Resultsmentioning

confidence: 99%

Algorithm for the precise detection of single and cluster cells in microfluidic applications

et al. 2016

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Recent advances in imaging flow cytometry and microfluidic applications have led to the development of suitable mathematical algorithms capable of detecting and identifying targeted cells in images. In contrast to currently existing algorithms, we herein proposed the identification and reconstruction of cell edges based on original approaches that overcome frequent detection limitations such as halos, noise, and droplet boundaries in microfluidic applications. Reconstructed cells are then discriminated between single cells and clusters of round-shaped cells, and cell information such as the area and location of a cell in an image is output. Using this method, 76% of cells detected in an image had an error <5% of the cell area size and 41% of the image had an error <1% of the cell area size (n 5 1,000). The method developed in the present study is the first image processing algorithm designed to be flexible in use (i.e. independent of the size of an image, using a microfluidic droplet system or not, and able to recognize cell clusters in an image) and provides the scientific community with a very accurate imaging algorithm in the field of microfluidic applications. V C 2016 International Society for Advancement of CytometryKey terms algorithm; microfluidic; cell detection; cell reconstruction; imaging processing; droplet THE morphological identification of a cell offers numerous advantages when discriminating elements such as cell populations based on their shapes. However, this time-consuming method is limited to the skills of the operator and is associated with difficulty in imaging cell sorters where quick decisions must be made based on the information contained in each image. The combination of a high speed camera and algorithm has been proposed in order to routinely and rapidly measure the concentration of target cells in a sample. This recent imaging cytometric approach associated with microfluidic devices indicates that the automatic detection of a cell in an image is applicable to the identification of rare cell types (e.g., possible circulating tumor cells) without any fluorescence label (1). In this context, the morphological properties of a cell such as size area, diameter, and circularity must be calculated with the highest precision in order to limit inaccurate diagnoses.In the image processing field, numerous algorithms have been created and improved with the aim of correctly identifying the edge of an object and providing reliable data in greyscale and color images (2-7). However, in imaging flow cytometric systems and microfluidic devices, cells moving in the flow may hamper these common edge detectors and led to the incomplete detection of cell walls. To overcome this limitation and improve the detection of a cell in an image, several specific algorithms have been developed in the past decade (8-11). Although these algorithms provided interesting findings, some common limitations remained. In point of view of the flexibility in use, development of specific algorithms leads to focus on ca...

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Section: Resultsmentioning

confidence: 99%

Algorithm for the precise detection of single and cluster cells in microfluidic applications

et al. 2016

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“…The technique has been used in previous reports regarding EMP cytotoxicity (Engstrom et al 1991;Behnam-Motlagh et al 1997;Behnam-Motlagh et al 2000). In short, we used a special type of microperifusion chamber consisting of glass slides forming a compartment in which cancer cells could be perifused.…”

Section: Methodsmentioning

confidence: 99%

Ondansetron but not granisetron affect cell volume regulation and potassium ion transport of glioma cells treated with estramustine phosphate

Behnam‐Motlagh

Sandström

Henriksson

et al. 2002

Journal of Cancer Research and Clinical Oncology

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“…The most commonly used of which is form factor (Berezin et al ., ; Soll et al ., ; Lepekhin et al ., ) also called circularity (Schneider et al ., ) or compactness (Metzler et al ., ), being a ratio between the area and the square of the perimeter, as shown in Table . Sometimes it is inappropriately used with its reciprocal, roundness (Behnam‐Motlagh et al ., ). Both descriptors are rotation invariant and size‐independent indicators of the degree of cell circularity and shape irregularities.…”

Section: Morphometric Descriptorsmentioning

confidence: 97%

“…The most commonly used of which is form factor Soll et al, 1988;Lepekhin et al, 2001) also called circularity (Schneider et al, 2012) or compactness (Metzler et al, 2000), being a ratio between the area and the square of the perimeter, as shown in Table 1. Sometimes it is inappropriately used with its reciprocal, roundness (Behnam-Motlagh et al, 2000). Both descriptors are rotation invariant and size-independent indicators of the degree of cell circularity and shape ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi…”

Section: Simple Descriptorsmentioning

confidence: 99%

An insight into morphometric descriptors of cell shape that pertain to regenerative medicine

Lobo

See

Biggs

et al. 2015

J Tissue Eng Regen Med

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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 parameters of cell morphology will yield meaningful data, which methods should be utilized to categorize cell shape, and the extent of reliability of measurements and the interpretation of the resulting analysis. A large range of descriptors has been employed to objectively assess the cellular morphology in two-dimensional and three-dimensional domains. Intuitively, simple and applicable morphometric descriptors are preferable and standardized protocols for cell shape analysis can be achieved with the help of computerized tools. In this review, cellular morphology is discussed as a descriptor of cellular function and the current morphometric parameters that are used quantitatively in two- and three-dimensional environments are described. Furthermore, the current problems associated with these morphometric measurements are addressed. Copyright © 2015 John Wiley & Sons, Ltd.

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Response in shape and size of individual p31 cancer cells to cisplatin and ouabain: a computerized image analysis of cell halo characteristics during continuous perfusion

Cited by 8 publications

References 14 publications

Algorithm for the precise detection of single and cluster cells in microfluidic applications

Algorithm for the precise detection of single and cluster cells in microfluidic applications

Ondansetron but not granisetron affect cell volume regulation and potassium ion transport of glioma cells treated with estramustine phosphate

An insight into morphometric descriptors of cell shape that pertain to regenerative medicine

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