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analysis software columbus v 2 5  (Revvity)


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    Structured Review

    Revvity analysis software columbus v 2 5
    Analysis Software Columbus V 2 5, supplied by Revvity, used in various techniques. Bioz Stars score: 92/100, based on 56 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/image+data/Columbus+Image+Data+Storage+and+Analysis+System/pmc09683089-769-1-5
    Average 92 stars, based on 56 article reviews
    analysis software columbus v 2 5 - by Bioz Stars, 2026-09
    92/100 stars

    Images

    Related Articles

    Fluorescence:

    Article Title: Inhibition of an immunometabolic axis of mTORC1 activation extends mammalian healthspan
    Article Snippet: Each condition was imaged from duplicated wells and a minimum of 7 fields/well using Operetta high-content imaging system 1483 (PerkinElmer). .. The measurement of p16 and p21 fluorescence intensity per area (normalised to the number of cells) was performed with Columbus 2.9 (PerkinElmer). ..

    Quantitation Assay:

    Article Title: Chemogenetic profiling of ubiquitin-like modifier pathways identifies NFATC2IP as a mediator of SUMO-dependent genome integrity
    Article Snippet: Plates were scanned for image acquisitions on an InCell Analyzer 6000 automated microscope (GE Healthcare) with 20x or 60x objectives. .. Image analyses for micronuclei quantitation were performed using Columbus image storage and analysis software (Perkin Elmer). ..

    Software:

    Article Title: Chemogenetic profiling of ubiquitin-like modifier pathways identifies NFATC2IP as a mediator of SUMO-dependent genome integrity
    Article Snippet: Plates were scanned for image acquisitions on an InCell Analyzer 6000 automated microscope (GE Healthcare) with 20x or 60x objectives. .. Image analyses for micronuclei quantitation were performed using Columbus image storage and analysis software (Perkin Elmer). ..

    Article Title: The eIF4A2 negative regulator of mRNA translation promotes extracellular matrix deposition to accelerate hepatocellular carcinoma initiation
    Article Snippet: For fibronectin analysis, cell-derived matrices were blocked with 1% BSA and stained with FN antibody (BD Pharmingen 610078 1:100) for 2h at RT and then with secondary antibody, Alexa-555 (Thermo Fisher 1:400) for 45 min at RT. .. Fibronectin was imaged at ×20 magnification using the Opera Phenix High-Content Screening System (Harmony High-content Imaging and analysis software version 4.9, PerkinElmer) and fibronectin fibers were quantified using Columbus Image Data Storage and Analysis System (PerkinElmer version 2.8.0). ..

    Microscopy:

    Article Title: Gymnotic uptake of AntimiRs alter microRNA-34a levels in 2D and 3D epithelial cell culture
    Article Snippet: The plates were sealed with black plate sealers (PerkinElmer, Waltham, MAcat., no. 6050173) and stored at 4°C until analysis. .. The imagining was done using a Yokogawa CV7000 confocal microscope and image and data analysis were carried out using Columbus (PerkinElmer) and R. ..

    Article Title: An oxadiazole-based compound potentiates anti-tuberculosis treatment by increasing host resistance via zinc poisoning
    Article Snippet: After 1 h, cells were washed and stained with HCS CellMask Blue stain (2 μg/mL, Thermo Fisher) and Hoechst 33342 (5 μg/mL, Thermo Fisher) in PBS for 30 minutes at room temperature, washed twice with PBS and stored at 4 °C until acquisition. .. Images were acquired using the automated confocal microscope Opera Phenix High-Content Screening System (Perkin Elmer Technology) with a 40x/NA 1.1 water objective followed by image analysis (Columbus ConductorTM Database, Perkin Elmer Technologies). ..

    High Content Screening:

    Article Title: An oxadiazole-based compound potentiates anti-tuberculosis treatment by increasing host resistance via zinc poisoning
    Article Snippet: After 1 h, cells were washed and stained with HCS CellMask Blue stain (2 μg/mL, Thermo Fisher) and Hoechst 33342 (5 μg/mL, Thermo Fisher) in PBS for 30 minutes at room temperature, washed twice with PBS and stored at 4 °C until acquisition. .. Images were acquired using the automated confocal microscope Opera Phenix High-Content Screening System (Perkin Elmer Technology) with a 40x/NA 1.1 water objective followed by image analysis (Columbus ConductorTM Database, Perkin Elmer Technologies). ..

    Article Title: The eIF4A2 negative regulator of mRNA translation promotes extracellular matrix deposition to accelerate hepatocellular carcinoma initiation
    Article Snippet: For fibronectin analysis, cell-derived matrices were blocked with 1% BSA and stained with FN antibody (BD Pharmingen 610078 1:100) for 2h at RT and then with secondary antibody, Alexa-555 (Thermo Fisher 1:400) for 45 min at RT. .. Fibronectin was imaged at ×20 magnification using the Opera Phenix High-Content Screening System (Harmony High-content Imaging and analysis software version 4.9, PerkinElmer) and fibronectin fibers were quantified using Columbus Image Data Storage and Analysis System (PerkinElmer version 2.8.0). ..

    other:

    Article Title: Uptake and anti-inflammatory effects of liposomal astaxanthin on endothelial cells tracked by Raman and fluorescence imaging
    Article Snippet: ImageJ (National Institutes of Health; http://rsbweb.nih.gov/ij/ ) and Columbus Image Data Storage and Analysis System (Perkin Elmer) were used for the processing of the fluorescence images.

    Imaging:

    Article Title: The eIF4A2 negative regulator of mRNA translation promotes extracellular matrix deposition to accelerate hepatocellular carcinoma initiation
    Article Snippet: For fibronectin analysis, cell-derived matrices were blocked with 1% BSA and stained with FN antibody (BD Pharmingen 610078 1:100) for 2h at RT and then with secondary antibody, Alexa-555 (Thermo Fisher 1:400) for 45 min at RT. .. Fibronectin was imaged at ×20 magnification using the Opera Phenix High-Content Screening System (Harmony High-content Imaging and analysis software version 4.9, PerkinElmer) and fibronectin fibers were quantified using Columbus Image Data Storage and Analysis System (PerkinElmer version 2.8.0). ..



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    Results of JSW measurements. In the 32 hips analyzed in this study, the mean JSW measured on MRI was 4.2 ± 1.4 mm on T1-weighted images (T1WI), 3.0 ± 1.3 mm on STIR, and 3.3 ± 0.9 mm on MERGE images. On CT images, the mean JSW was 3.4 ± 1.1 mm. CT, Computed tomography; <t>MERGE,</t> <t>Multi-echo</t> recombined gradient echo; STIR, Short tau inversion-recovery; T1-WI, T1-weighted imaging
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    Image Search Results


    Illustration of 22 varieties of panicle images of primitive paddy.

    Journal: Data in Brief

    Article Title: A benchmark dataset of Primitive Indian Paddy Panicle Images and identification via deep residual transfer learning

    doi: 10.1016/j.dib.2026.112774

    Figure Lengend Snippet: Illustration of 22 varieties of panicle images of primitive paddy.

    Article Snippet: Mendeley Data Primitive Indian Paddy Panicle Images (Original data) .

    Techniques:

    Class distribution of the Dry Fish Dataset.

    Journal: Data in Brief

    Article Title: Dry Fish Image dataset: Data-driven analysis and deep learning-based classification

    doi: 10.1016/j.dib.2026.112683

    Figure Lengend Snippet: Class distribution of the Dry Fish Dataset.

    Article Snippet: Mendeley Data Dry Fish Image Dataset (Original data) .

    Techniques:

    The four categories of image samples included in the Fire Recognition Image Dataset . Top row: (a) Fire – images showing active flames; (b) Smoke – images containing visible smoke plumes. Bottom row: (c) Safe Fire – non-hazardous scenes such as candles or stoves; (d) Artificial Fire – simulated or decorative fire displays.

    Journal: Data in Brief

    Article Title: An open-access fire detection image dataset for research in computer vision and safety monitoring

    doi: 10.1016/j.dib.2026.112801

    Figure Lengend Snippet: The four categories of image samples included in the Fire Recognition Image Dataset . Top row: (a) Fire – images showing active flames; (b) Smoke – images containing visible smoke plumes. Bottom row: (c) Safe Fire – non-hazardous scenes such as candles or stoves; (d) Artificial Fire – simulated or decorative fire displays.

    Article Snippet: Mendeley Data Fire Recognition Image Dataset (Original data)

    Techniques:

    Workflow illustrating the step-by-step process for generating the Fire Recognition Image Dataset, from video selection to dataset organization and augmentation.

    Journal: Data in Brief

    Article Title: An open-access fire detection image dataset for research in computer vision and safety monitoring

    doi: 10.1016/j.dib.2026.112801

    Figure Lengend Snippet: Workflow illustrating the step-by-step process for generating the Fire Recognition Image Dataset, from video selection to dataset organization and augmentation.

    Article Snippet: Mendeley Data Fire Recognition Image Dataset (Original data)

    Techniques: Selection

    Sample images of AsianVehicle each class.

    Journal: Data in Brief

    Article Title: AsianVehicle: An image dataset of traditional Asian vehicles

    doi: 10.1016/j.dib.2026.112709

    Figure Lengend Snippet: Sample images of AsianVehicle each class.

    Article Snippet: Mendeley Data AsianVehicle: Image Dataset of Traditional Asian Vehicles for Computer Vision (Original data) .

    Techniques:

    Cumulative \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document} over communication rounds for the COVID-19 dataset with target \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon = 1.9$$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta = 10^{-5}$$\end{document} . The RDP accountant tracks multiple \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} orders and yields the final \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document} at round 75.

    Journal: Scientific Reports

    Article Title: Federated learning with swarm intelligence for efficient and secure medical image analysis

    doi: 10.1038/s41598-026-50882-8

    Figure Lengend Snippet: Cumulative \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document} over communication rounds for the COVID-19 dataset with target \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon = 1.9$$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta = 10^{-5}$$\end{document} . The RDP accountant tracks multiple \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} orders and yields the final \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document} at round 75.

    Article Snippet: This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms.

    Techniques:

    Performance analysis for Hospital Model A (Client A) across COVID-19, Monkeypox, and Breast Cancer datasets. The radar charts display accuracy, precision, recall, specificity, and F1-score metrics for different CNN-optimization combinations.

    Journal: Scientific Reports

    Article Title: Federated learning with swarm intelligence for efficient and secure medical image analysis

    doi: 10.1038/s41598-026-50882-8

    Figure Lengend Snippet: Performance analysis for Hospital Model A (Client A) across COVID-19, Monkeypox, and Breast Cancer datasets. The radar charts display accuracy, precision, recall, specificity, and F1-score metrics for different CNN-optimization combinations.

    Article Snippet: This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms.

    Techniques:

    Performance evaluation for Hospital Model B (Client B) showing superior COVID-19 detection capabilities with VGG16 + PSO and exceptional Monkeypox classification using Inception + PSO across all evaluation metrics.

    Journal: Scientific Reports

    Article Title: Federated learning with swarm intelligence for efficient and secure medical image analysis

    doi: 10.1038/s41598-026-50882-8

    Figure Lengend Snippet: Performance evaluation for Hospital Model B (Client B) showing superior COVID-19 detection capabilities with VGG16 + PSO and exceptional Monkeypox classification using Inception + PSO across all evaluation metrics.

    Article Snippet: This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms.

    Techniques:

    Hospital Model C (Client C) performance analysis demonstrating exceptional consistency across all medical datasets, with VGG16 + PSO excelling in COVID-19 detection and Inception + PSO achieving superior Monkeypox classification accuracy.

    Journal: Scientific Reports

    Article Title: Federated learning with swarm intelligence for efficient and secure medical image analysis

    doi: 10.1038/s41598-026-50882-8

    Figure Lengend Snippet: Hospital Model C (Client C) performance analysis demonstrating exceptional consistency across all medical datasets, with VGG16 + PSO excelling in COVID-19 detection and Inception + PSO achieving superior Monkeypox classification accuracy.

    Article Snippet: This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms.

    Techniques:

    Hospital Model D (Client D) performance evaluation showing exceptional COVID-19 detection with VGG16 + PSO achieving 96.71% accuracy and superior Monkeypox classification using Inception + PSO with balanced precision and recall metrics.

    Journal: Scientific Reports

    Article Title: Federated learning with swarm intelligence for efficient and secure medical image analysis

    doi: 10.1038/s41598-026-50882-8

    Figure Lengend Snippet: Hospital Model D (Client D) performance evaluation showing exceptional COVID-19 detection with VGG16 + PSO achieving 96.71% accuracy and superior Monkeypox classification using Inception + PSO with balanced precision and recall metrics.

    Article Snippet: This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms.

    Techniques:

    Results of JSW measurements. In the 32 hips analyzed in this study, the mean JSW measured on MRI was 4.2 ± 1.4 mm on T1-weighted images (T1WI), 3.0 ± 1.3 mm on STIR, and 3.3 ± 0.9 mm on MERGE images. On CT images, the mean JSW was 3.4 ± 1.1 mm. CT, Computed tomography; MERGE, Multi-echo recombined gradient echo; STIR, Short tau inversion-recovery; T1-WI, T1-weighted imaging

    Journal: European Radiology Experimental

    Article Title: Feasibility of bone-like MRI for acetabular morphology assessment in developmental dysplasia of the hip

    doi: 10.1186/s41747-026-00725-y

    Figure Lengend Snippet: Results of JSW measurements. In the 32 hips analyzed in this study, the mean JSW measured on MRI was 4.2 ± 1.4 mm on T1-weighted images (T1WI), 3.0 ± 1.3 mm on STIR, and 3.3 ± 0.9 mm on MERGE images. On CT images, the mean JSW was 3.4 ± 1.1 mm. CT, Computed tomography; MERGE, Multi-echo recombined gradient echo; STIR, Short tau inversion-recovery; T1-WI, T1-weighted imaging

    Article Snippet: Similar sequences from other manufacturers include “multi-echo data image combination”‒MEDIC from Siemens Healthineers and “merged fast field echo”‒M-FFE from Philips Healthcare.

    Techniques: Computed Tomography, Imaging

    Bland–Altman analysis comparing MERGE MRI and CT measurements. a Bland–Altman plot showing agreement between MERGE MRI and CT measurements for JSW. The solid horizontal line represents the mean difference (-0.25 mm), and the dashed lines indicate the 95% limits of agreement (-2.04 to 1.54 mm). No proportional bias was observed across the measurement range. b Bland–Altman plot showing agreement between MERGE MRI and CT measurements for the CE angle. The solid horizontal line represents the mean difference (-0.09°), and the dashed lines indicate the 95% limits of agreement (-9.85° to 9.66°). No systematic or proportional bias was observed. CE, Center-edge; CT, computed tomography; MERGE, Multi-echo recombined gradient echo

    Journal: European Radiology Experimental

    Article Title: Feasibility of bone-like MRI for acetabular morphology assessment in developmental dysplasia of the hip

    doi: 10.1186/s41747-026-00725-y

    Figure Lengend Snippet: Bland–Altman analysis comparing MERGE MRI and CT measurements. a Bland–Altman plot showing agreement between MERGE MRI and CT measurements for JSW. The solid horizontal line represents the mean difference (-0.25 mm), and the dashed lines indicate the 95% limits of agreement (-2.04 to 1.54 mm). No proportional bias was observed across the measurement range. b Bland–Altman plot showing agreement between MERGE MRI and CT measurements for the CE angle. The solid horizontal line represents the mean difference (-0.09°), and the dashed lines indicate the 95% limits of agreement (-9.85° to 9.66°). No systematic or proportional bias was observed. CE, Center-edge; CT, computed tomography; MERGE, Multi-echo recombined gradient echo

    Article Snippet: Similar sequences from other manufacturers include “multi-echo data image combination”‒MEDIC from Siemens Healthineers and “merged fast field echo”‒M-FFE from Philips Healthcare.

    Techniques: Computed Tomography

    Results of CE angle measurements. In the 32 hips analyzed, the mean CE angle measured on MRI was 29.7 ± 9.5° on T1WI, 30.9 ± 9.9° on STIR, and 31.4 ± 10.1° on MERGE images. On CT, the mean CE angle was 31.4 ± 10.1°. CT, Computed tomography; MERGE, Multi-echo recombined gradient echo; STIR, Short tau inversion-recovery; T1-WI, T1-weighted imaging

    Journal: European Radiology Experimental

    Article Title: Feasibility of bone-like MRI for acetabular morphology assessment in developmental dysplasia of the hip

    doi: 10.1186/s41747-026-00725-y

    Figure Lengend Snippet: Results of CE angle measurements. In the 32 hips analyzed, the mean CE angle measured on MRI was 29.7 ± 9.5° on T1WI, 30.9 ± 9.9° on STIR, and 31.4 ± 10.1° on MERGE images. On CT, the mean CE angle was 31.4 ± 10.1°. CT, Computed tomography; MERGE, Multi-echo recombined gradient echo; STIR, Short tau inversion-recovery; T1-WI, T1-weighted imaging

    Article Snippet: Similar sequences from other manufacturers include “multi-echo data image combination”‒MEDIC from Siemens Healthineers and “merged fast field echo”‒M-FFE from Philips Healthcare.

    Techniques: Computed Tomography, Imaging

    Representative clinical case. A 23-year-old female patient underwent MRI and CT examinations several months after right hip PAO. On coronal images: ( a ) T1WI shows a JSW of 4.9 mm; ( b ) STIR shows 3.6 mm; ( c ) MERGE shows 4.0 mm; and ( d ) CT shows 4.2 mm. The CE angles were: 36° on T1WI, 33° on STIR, 30° on MERGE, and 30° on CT. CT, Computed tomography; MERGE, Multi-echo recombined gradient echo; STIR, Short tau inversion-recovery; T1-WI, T1-weighted imaging

    Journal: European Radiology Experimental

    Article Title: Feasibility of bone-like MRI for acetabular morphology assessment in developmental dysplasia of the hip

    doi: 10.1186/s41747-026-00725-y

    Figure Lengend Snippet: Representative clinical case. A 23-year-old female patient underwent MRI and CT examinations several months after right hip PAO. On coronal images: ( a ) T1WI shows a JSW of 4.9 mm; ( b ) STIR shows 3.6 mm; ( c ) MERGE shows 4.0 mm; and ( d ) CT shows 4.2 mm. The CE angles were: 36° on T1WI, 33° on STIR, 30° on MERGE, and 30° on CT. CT, Computed tomography; MERGE, Multi-echo recombined gradient echo; STIR, Short tau inversion-recovery; T1-WI, T1-weighted imaging

    Article Snippet: Similar sequences from other manufacturers include “multi-echo data image combination”‒MEDIC from Siemens Healthineers and “merged fast field echo”‒M-FFE from Philips Healthcare.

    Techniques: Computed Tomography, Imaging