A Framework for Automated Cell Tracking in Phase Contrast Microscopic Videos based on Normal Velocities

TL;DR

This paper presents a new automated cell tracking framework for phase contrast microscopy videos that leverages topology-preserving segmentation and optical flow to robustly track diverse cell types, validated against manual annotations.

Contribution

The novel framework combines variational segmentation on normal velocities with correction via active contours, improving robustness and accuracy in challenging microscopic videos.

Findings

Robust cell tracking across different cell types and conditions.

Effective correction step enhances shape feature extraction.

Validated results show high agreement with manual tracking.

Abstract

This paper introduces a novel framework for the automated tracking of cells, with a particular focus on the challenging situation of phase contrast microscopic videos. Our framework is based on a topology preserving variational segmentation approach applied to normal velocity components obtained from optical flow computations, which appears to yield robust tracking and automated extraction of cell trajectories. In order to obtain improved trackings of local shape features we discuss an additional correction step based on active contours and the image Laplacian which we optimize for an example class of transformed renal epithelial (MDCK-F) cells. We also test the framework for human melanoma cells and murine neutrophil granulocytes that were seeded on different types of extracellular matrices. The results are validated with manual tracking results.