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Simplified, automated methods for assessing pixel intensities of fluorescently-tagged drugs in cells
by
Jiang, Meiyan
, Kachelmeier, Allan
, Shola, Tsering
, Meier, William B.
, Steyger, Peter S.
, Johnson, Anastasiya
in
Algebra
/ Algorithms
/ Animals
/ Artificial intelligence
/ Autocorrelation
/ Automation
/ Automation, Laboratory - methods
/ Batch processing
/ Bioinformatics
/ Biology and Life Sciences
/ Confocal microscopy
/ Convolution
/ Data processing
/ Drug screening
/ Drugs
/ Fluorescent Dyes
/ Geography
/ Image processing
/ Image Processing, Computer-Assisted - methods
/ Image segmentation
/ Inner ear
/ Laboratories
/ Masking
/ Methods
/ Microscopy
/ Microscopy, Confocal - methods
/ Morphology
/ Normal distribution
/ Parenchyma
/ Pattern recognition
/ Pattern Recognition, Automated - methods
/ Physical Sciences
/ Pixels
/ Research and Analysis Methods
/ Software
/ Stria vascularis
2018
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Simplified, automated methods for assessing pixel intensities of fluorescently-tagged drugs in cells
by
Jiang, Meiyan
, Kachelmeier, Allan
, Shola, Tsering
, Meier, William B.
, Steyger, Peter S.
, Johnson, Anastasiya
in
Algebra
/ Algorithms
/ Animals
/ Artificial intelligence
/ Autocorrelation
/ Automation
/ Automation, Laboratory - methods
/ Batch processing
/ Bioinformatics
/ Biology and Life Sciences
/ Confocal microscopy
/ Convolution
/ Data processing
/ Drug screening
/ Drugs
/ Fluorescent Dyes
/ Geography
/ Image processing
/ Image Processing, Computer-Assisted - methods
/ Image segmentation
/ Inner ear
/ Laboratories
/ Masking
/ Methods
/ Microscopy
/ Microscopy, Confocal - methods
/ Morphology
/ Normal distribution
/ Parenchyma
/ Pattern recognition
/ Pattern Recognition, Automated - methods
/ Physical Sciences
/ Pixels
/ Research and Analysis Methods
/ Software
/ Stria vascularis
2018
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Simplified, automated methods for assessing pixel intensities of fluorescently-tagged drugs in cells
by
Jiang, Meiyan
, Kachelmeier, Allan
, Shola, Tsering
, Meier, William B.
, Steyger, Peter S.
, Johnson, Anastasiya
in
Algebra
/ Algorithms
/ Animals
/ Artificial intelligence
/ Autocorrelation
/ Automation
/ Automation, Laboratory - methods
/ Batch processing
/ Bioinformatics
/ Biology and Life Sciences
/ Confocal microscopy
/ Convolution
/ Data processing
/ Drug screening
/ Drugs
/ Fluorescent Dyes
/ Geography
/ Image processing
/ Image Processing, Computer-Assisted - methods
/ Image segmentation
/ Inner ear
/ Laboratories
/ Masking
/ Methods
/ Microscopy
/ Microscopy, Confocal - methods
/ Morphology
/ Normal distribution
/ Parenchyma
/ Pattern recognition
/ Pattern Recognition, Automated - methods
/ Physical Sciences
/ Pixels
/ Research and Analysis Methods
/ Software
/ Stria vascularis
2018
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Simplified, automated methods for assessing pixel intensities of fluorescently-tagged drugs in cells
Journal Article
Simplified, automated methods for assessing pixel intensities of fluorescently-tagged drugs in cells
2018
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Overview
Assessing the cytoplasmic uptake of fluorescently-tagged drugs in heterogeneous cell types currently involves time-consuming manual segmentation of confocal microscopy images. We developed a set of methods that incorporate map algebra techniques to facilitate and expedite image segmentation, particularly of the parenchyma of intermediate cells in the stria vascularis of the inner ear. Map algebra is used to apply a convolution kernel to pixel neighborhoods to create a masking image to select pixels in the original image for further operations. Here, we describe the utility of integrated intensity-based, percentile-based, and local autocorrelation-based methods to automate segmentation of images into putative morphological regions for pixel intensity analysis. Integrated intensity-based methods are variants of watershed segmentation tools that determine morphological boundaries from rates of change in integrated pixel intensity. Percentile- and local autocorrelation-based methods evolved out of the process of developing map algebra- and integrated intensity-based tools. We identified several simplifications that are surprisingly effective for image segmentation and pixel intensity analysis. These methods were empirically validated on three levels: first, the algorithms were developed based on iterations of inspected results; second, algorithms were tested for various types of robustness; and third, developed algorithms were validated against results from manually-segmented images. We conclude the key to automated segmentation is supervision of output data.
Publisher
Public Library of Science,Public Library of Science (PLoS)
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