![]() ![]() Here we used multiple separate observers’ complete manual counts for comparison to an automatic cell counting methodology. These selective image analysis techniques can provide valuable physiological information and manual counts by trained professionals have been held up as the “gold standard” for quantification 1, 2. Quantifying cells in immunofluorescent images has long been a limiting step in both time and required effort for the analysis of microscopy data used in research. For comparison, both datasets were manually counted to demonstrate the applicability of ACCT as an accessible means to automatically quantify cells in a precise manner without the need for computing clusters or advanced data preparation. ACCT is demonstrated with a comparative analysis of publicly available images of neurons and an in-house dataset of immunofluorescence-stained microglia cells. Thus, we introduce a novel tool ACCT: Automatic Cell Counting with Trainable Weka Segmentation which allows for flexible automatic cell counting via object segmentation after user-driven training. While tools exist to automatically count cells in images, the accuracy and accessibility of such tools can be improved. A common approach for this process is having trained researchers individually select and count cells within an image, which is not only difficult to standardize but also very time-consuming. Counting cells is a cornerstone of tracking disease progression in neuroscience.
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