ArXiv Preprint
The automated segmentation and tracking of macrophages during their migration
are challenging tasks due to their dynamically changing shapes and motions.
This paper proposes a new algorithm to achieve automatic cell tracking in
time-lapse microscopy macrophage data. First, we design a segmentation method
employing space-time filtering, local Otsu's thresholding, and the SUBSURF
(subjective surface segmentation) method. Next, the partial trajectories for
cells overlapping in the temporal direction are extracted in the segmented
images. Finally, the extracted trajectories are linked by considering their
direction of movement. The segmented images and the obtained trajectories from
the proposed method are compared with those of the semi-automatic segmentation
and manual tracking. The proposed tracking achieved 97.4% of accuracy for
macrophage data under challenging situations, feeble fluorescent intensity,
irregular shapes, and motion of macrophages. We expect that the automatically
extracted trajectories of macrophages can provide pieces of evidence of how
macrophages migrate depending on their polarization modes in the situation,
such as during wound healing.
Seol Ah Park, Tamara Sipka, Zuzana Kriva, George Lutfalla, Mai Nguyen-Chi, Karol Mikula
2023-01-02
