Cell tracking under high confluency conditions by candidate cell region detection-based association approach

Automated tracking of cell population is an important element of research and discovery in the biology field. In this paper, we propose a method that tracks cells under highly confluent conditions by using the candidate cell region detection-based association approach. Unlike conventional segmentation-based association tracking methods, the proposed method uses the tracking results from the previous frame to segment the cell regions at the current frame. First, candidate cell regions are detected, and while there may be many false positives, there are very few false negatives. Next, optimized detection results are selected from the candidate regions and associated with the tracking results of the previous frame by resolving a linear programming problem. We quantitatively evaluated the proposed method using a variety of sequences. Results showed that our method has a better tracking performance than conventional segmentation-based association methods.