Segmentation based tracking of cells in 2D+time microscopy images of macrophages

TL;DR

This paper introduces a novel automated method for segmenting and tracking macrophages in 2D+time microscopy images, achieving high accuracy despite challenging imaging conditions.

Contribution

The paper presents a new algorithm combining space-time filtering, local Otsu's thresholding, and SUBSURF segmentation for automatic macrophage tracking in microscopy data.

Findings

Achieved 97.4% accuracy in macrophage tracking

Effective in challenging conditions like low fluorescence and irregular shapes

Provides reliable trajectories for studying macrophage migration

Abstract

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.