Evaluation of colony‐forming ability experiments using normal and DNA repair‐deficient human fibroblast strains and an automatic colony counter

Thielmann, Heinz Walter; Hagedorn, Roland; Freber, Werner

doi:10.1002/cyto.990060208

Cited by 10 publications

(9 citation statements)

References 17 publications

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“…Several authors have noted the problems associated with detecting colonies around the periphery of a culture flask and several have resorted to masking this area to exclude it from processing. Counting colonies that merge into one another has also proved to be problematic, and statistical corrections have been employed to reduce systematic errors (Thielmann and Hagedorn 1985). Approaches to overcome the problems of merging and less-discrete or fuzzy colonies giving multiple counts have resulted in total-colony-area based statistical counts (Mukherjee et al 1995).…”

Section: Introductionmentioning

confidence: 99%

Automated counting of mammalian cell colonies

et al. 2000

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Investigating the effect of low-dose radiation exposure on cells using assays of colony-forming ability requires large cell samples to maintain statistical accuracy. Manually counting the resulting colonies is a laborious task in which consistent objectivity is hard to achieve. This is true especially with some mammalian cell lines which form poorly defined or 'fuzzy' colonies, typified by glioma or fibroblast cell lines. A computer-vision-based automated colony counter is presented in this paper. It utilizes novel imaging and image-processing methods involving a modified form of the Hough transform. The automated counter is able to identify less-discrete cell colonies typical of these cell lines. The results of automated colony counting are compared with those from four manual (human) colony counts for the cell lines HT29, A172, U118 and IN1265. The results from the automated counts fall well within the distribution of the manual counts for all four cell lines with respect to surviving fraction (SF) versus dose curves, SF values at 2 Gy (SF2) and total area under the SF curve (Dbar). From the variation in the counts, it is shown that the automated counts are generally more consistent than the manual counts.

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Section: Introductionmentioning

confidence: 99%

Automated counting of mammalian cell colonies

et al. 2000

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“…Although manual counting remains the gold standard, this tedious process is difficult to implement for highthroughput assays due to low speed and inconsistent results. Automation of colony counting has been of increasing interest for many decades (1)(2)(3), and these methods have been shown to be more consistent than manual counting (1,4). Herman et al (5) demonstrated that automated colony counts had significantly less variation when reanalyzing plates than those manually determined by individual or multiple observers.…”

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confidence: 99%

Low‐cost, high‐throughput, automated counting of bacterial colonies

et al. 2010

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Research involving bacterial pathogens often requires enumeration of bacteria colonies.Here, we present a low-cost, high-throughput colony counting system consisting of colony counting software and a consumer-grade digital camera or document scanner. We demonstrate that this software, called ''NICE'' (NIST's Integrated Colony Enumerator), can count bacterial colonies as part of a high-throughput multiplexed opsonophagocytic killing assay used to characterize pneumococcal vaccine efficacy. The results obtained with NICE correlate well with the results obtained from manual counting, with a mean difference of less than 3%. NICE is also rapid; it can count colonies from multiple reaction wells within minutes and export the results to a spreadsheet for data processing. As this program is freely available from NIST, NICE should be helpful in bacteria colony enumeration required in many microbiological studies, and in standardizing colony counting methods. Published 2010 Wiley-Liss, Inc.

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“…Accordingly, several automatic tools to segment images of cell colonies have been realized in the latest years to overcome this issue. Early automated approaches for clonogenic quantification have already appeared in the literature since the '80s [12][13][14][15][16][17]. In recent years, many other computational solutions were developed.…”

Section: Introductionmentioning

confidence: 99%

MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation

et al. 2020

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A clonogenic assay is a biological technique for calculating the Surviving Fraction (SF) that quantifies the anti-proliferative effect of treatments on cell cultures: this evaluation is often performed via manual counting of cell colony-forming units. Unfortunately, this procedure is error-prone and strongly affected by operator dependence. Besides, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or administered cytotoxic agent. Relying upon the direct proportional relationship between the Area Covered by Colony (ACC) and the colony count and size, along with the growth rate, we propose MF2C3, a novel computational method leveraging spatial Fuzzy C-Means clustering on multiple local features (i.e., entropy and standard deviation extracted from the input color images acquired by a general-purpose flat-bed scanner) for ACC-based SF quantification, by considering only the covering percentage. To evaluate the accuracy of the proposed fully automatic approach, we compared the SFs obtained by MF2C3 against the conventional counting procedure on four different cell lines. The achieved results revealed a high correlation with the ground-truth measurements based on colony counting, by outperforming our previously validated method using local thresholding on L*u*v* color well images. In conclusion, the proposed multi-feature approach, which inherently leverages the concept of symmetry in the pixel local distributions, might be reliably used in biological studies.

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Evaluation of colony‐forming ability experiments using normal and DNA repair‐deficient human fibroblast strains and an automatic colony counter

Cited by 10 publications

References 17 publications

Automated counting of mammalian cell colonies

Automated counting of mammalian cell colonies

Low‐cost, high‐throughput, automated counting of bacterial colonies

MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation

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