2008
DOI: 10.1002/cyto.a.20662
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Single‐cell‐based image analysis of high‐throughput cell array screens for quantification of viral infection

Abstract: The identification of eukaryotic genes involved in virus entry and replication is important for understanding viral infection. Our goal is to develop a siRNA-based screening system using cell arrays and high-throughput (HT) fluorescence microscopy. A central issue is efficient, robust, and automated single-cell-based analysis of massive image datasets. We have developed an image analysis approach that comprises (i) a novel, gradient-based thresholding scheme for cell nuclei segmentation which does not require … Show more

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Cited by 39 publications
(34 citation statements)
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References 32 publications
(41 reference statements)
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“…Image quality control is an important issue in automated imaging platforms (1). Low quality images need to be removed from data-sets before image analysis, because they decrease the counting precision and accuracy, as experimentally shown for bacterial cell counts (Fig.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Image quality control is an important issue in automated imaging platforms (1). Low quality images need to be removed from data-sets before image analysis, because they decrease the counting precision and accuracy, as experimentally shown for bacterial cell counts (Fig.…”
Section: Discussionmentioning
confidence: 99%
“…While it is easy to specify the in-focus image of cells in a z-stack of images (e.g., during autofocusing), it is much more demanding to derive focus information exclusively from a single image (1). This is even more difficult if it has to be specified a priori before actual object detection, i.e., without knowledge of the geometrical and densitometrical properties of the objects.…”
Section: Ann Input Datamentioning
confidence: 99%
“…In (Matula et al 2009) authors have developed an image analysis approach that comprises (i) a gradient-based thresholding scheme for cell nuclei segmentation which does not require subsequent postprocessing steps for separation of clustered nuclei, (ii) quantification of the virus signal in the neighborhood of cell nuclei, (iii) localization of regions with transfected cells by combining model-based circle fitting and grid fitting, (iv) cell classification as infected or noninfected, and (v) image quality control (e.g., identification of out-of-focus images). Due to it is difficult to find a single global threshold suitable for all nuclei, authors propose an edge-based approach which analyzes gradient magnitude images instead of thresholding the original image intensities.…”
Section: Segmentationmentioning
confidence: 99%
“…On a cellular level, cell-based screens have been developed to classify morphological phenotypes (18,19). This method has already been applied successfully to drug profiling (20), quantifying viral infection (21), and determining drug effects on cell adhesion (22).…”
mentioning
confidence: 99%