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Sadly, organoids have a top cellular thickness and fast cellular movement, which makes automated cell tracking difficult. In this work, a semi-automated cellular tracker is created. To detect the nuclei, we use a device learning approach considering a convolutional neural community. To make mobile trajectories, we connect detections at different time things collectively utilizing a min-cost flow solver. The tracker increases warnings for situations with most likely errors. Rapid alterations in nucle