arivis Scientific Image Analysis

Nuclei Counting

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Nuclei Counting


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Before and After Analysis

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Solution Description

Automated counting of nuclei is crucial for many applications and further downstream analyses. This solution is based on a pre-trained deep-learning model, automatically segmenting, separating, and counting nuclei based on any fluorescent nuclear marker such as DAPI in one or more microscopy images. It supports time series images as well as multi-well measurements. The output is a matrix that shows the number of nuclei per image in each well per time point (if applicable). The respective deep-learning model is trained on datasets from multiple microscopes with different resolutions & magnifications.


Figure 1: A heat map representation is generated, indicating the number of nuclei per image in each well across a 96-well plate (red: high, blue: low).

Deep learning, arivis Cloud, nuclei, counting, DAPI, Hoechst, fluorescence