A Method for Automatic Tracking of Cell Nuclei in 2D Epifluorescence Microscopy Image Sequences

The automated segmentation and tracking of cells in live cell microscopy image sequences is an actual problem in many biological research areas. Despite the existence of different cell tracking approaches, a universal solution for this problem still does not exist due to high variety of fluorescent microscopy image data obtained using different techniques, where cells have completely different visual appearance. Moreover, the cells can significantly change their shape even within a single image sequence. In this work, we propose a cell tracking algorithm designed for detecting and tracking cell nuclei in 2D image sequences obtained by epifluorescence microscopy, where the cell appearance drastically changes during cell mitosis. We used marker controlled watershed algorithm combined with blob detection for nuclei segmentation followed by a generalized nearest neighbor approach for nuclei tracking. We also employed a special mitosis detection algorithm to process cell division events. Our approach was quantitatively evaluated for its segmentation and tracking accuracy using the real image data annotated by human experts. The evaluation procedure was performed based on the protocol used in the Cell Tracking Challenge. It was shown that the proposed approach outperforms an existing semiautomatic method in both segmentation and tracking accuracy.