ImarisColoc by Bitplane AG (Part of the Andor Group)

Isolate, Visualize And Quantify Co-Localized Regions Obtaining accurate information about the position of stained tissue and cellular components is the primary goal of digital microscopy. ImarisColoc has been designed to give researchers the most powerful tool to quantify and document co-distribution of multiple stained biological components. Utilizing a choice of manual, semi – automatic, and automatic co-localization selection methods, ImrisColoc enables easy isolation, visualization, and quan... Read more
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ImarisColoc


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Isolate, Visualize And Quantify Co-Localized Regions

Obtaining accurate information about the position of stained tissue and cellular components is the primary goal of digital microscopy. ImarisColoc has been designed to give researchers the most powerful tool to quantify and document co-distribution of multiple stained biological components. Utilizing a choice of manual, semi – automatic, and automatic co-localization selection methods, ImrisColoc enables easy isolation, visualization, and quantification of regional overlap of multiple stains in 3D and 4D images.

Unlike most other commercial products, ImarisColoc helps you with the key decision point in the analysis. The start of any co-localization analysis is the exclusion of regions that will only add noise and no signal. ImarisColoc provides possibilities to do this via masking out regions and by excluding certain intensity ranges. Masking can be completed within ImarisColoc using the intensities of one of the channels being analyzed or any other channel. Masking can also be completed as part of the functions of Imaris MeasurementPro. The determination of intensities to include / exclude from the study, i.e. the threshold selection, is achieved by thresholding the source channels used in the analysis. Several manual procedures such as selection in a scatter plot, selection in a histogram, or semi-automatic selection in the image itself can be used but these methods naturally bring along the risk of introducing user biases.