Analysis: Image Quantification

Answering scientific questions based on manual image analysis can be complicated and inefficient. Numerous tools exist to efficiently analyze images in a way that can answer your scientific question. However, at a minimum, raw data must be processed through multiple steps, removing systematic or sample-induced artifacts to reach a point where statistics can be uniformly applied to the data set.

We offer technology that removes artifacts, segments images, and analyzes relevant biology methodically and precisely to ensure faithful recapitulation of the true biological signal.

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Removing artifacts

Images are firstcaptured by detectors that convert photons into electrical signals. These analog electrical signals are then converted into digital readings that are portrayed as an array.  However, these electrical signals come not only from photons produced by the sample of interest but also from confounding sources, such as autofluorescence or systemic optical aberrations. Each confounding source needs correction to reveal the true signal produced by the sample.

The corrective manipulations must be performed on the raw images in a precise series to ensure an accurate representation of the true biological signal. Integrated software solutions that perform these corrections as the data are generated both (1) provide a measure of objectivity and (2) help speed the image analysis process.

Identifying relevant biology 

After an image has been sufficiently processed, the next step is to identify the biological activity of interest, which is done through appropriate image masking. In the simplest method for image masking, called “thresholding,” pixels are analyzed if above, or disregarded if below, a specific threshold. More complex interactions may also be analyzed using multiple masks controlled by Boolean logic (e.g., AND, OR, NOT) to hone in on the exact pixels of interest. Again, though, these analyses can be time-consuming and subjective. Purpose-built software that presents only the tools necessary for evaluation of data relevant to a specific scientific question can make image analysis both more objective and faster.

Analyzing large data sets

Raw data must be processed through multiple steps to reach a point where statistics may be meaningfully applied to the set. In a live-cell experiment performed in a 96-well plate, a thousand images is a perfectly reasonable data set size (and can be larger if you are capturing multiple channels, e.g. red fluorescence/green fluorescence/transmitted light). In analyzing a large image set, one must be assured that the set of operations is suitable across the set (e.g., on dead or living cells). Traditional image analysis software does not offer the ability to assess a variety of images in an efficient manner thus analyzing typical live-cell microplate assays can be unwieldy. Our software has built-in solutions to address your needs when performing all the steps required to convert raw images to actionable data at scale.

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