The automated tracking of subcellular structures in live microscopy image sequences is an actual problem in many biological research areas. A universal solution for this problem still does not exist due to a huge variety of data of different nature. In this work, we propose an algorithm for tracking actin filaments in 2D fluorescent image sequences. The filaments are moving in a random and abrupt manner frequently crossing each other. We used steerable filters based ridge detection followed by crossing filaments correction algorithm for filaments detection. The tracking was performed using a greedy nearest neighbor method. The quantitative evaluation of our approach was performed on several manually annotated image sequences using the object tracking quality metric MOTA. It was shown that the proposed approach outperforms an existing approach in tracking accuracy. In addition, the proposed approach allows processing crossed filaments, unlike the existing methods.
Automated Analysis of the Mitotic Phases of Human Cells in 3D Fluorescence Microscopy Image Sequences
