Calcium imaging can record from several dozens of neurons at once. Analyzing this raw data is expensive, so one typically wants to define regions of interest corresponding to cell bodies and work with the average calcium signal within.
Dario has a post on defining polygonal ROIs using the mean fluorescence image. Doing this manually is fairly time-consuming and it can be easy to miss perfectly good cells. Automated sorting methods still require some oversight, which can quickly become as time-consuming as defining the ROIs manually.
I’ve worked on an enhanced method that makes defining an ROI as simple as two clicks. The first enhancement is to use other reference images in adding to the mean fluorescence image: the correlation image, standard deviation over the mean, and kurtosis. The correlation image, discussed earlier on Labrigger, shows how correlated a single pixel is with its neighbour. When adjacent pixels are strongly correlated, that’s a good sign that that pixel belongs to a potential…
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