  Automatic detection and tracking of cells in microscopy images are major
applications of computer vision technologies in both biomedical research and
clinical practice. Though machine learning methods are increasingly common in
these fields, classical algorithms still offer significant advantages for both
tasks, including better explainability, faster computation, lower hardware
requirements and more consistent performance. In this paper, we present a novel
approach based on gravitational force fields that can compete with, and
potentially outperform modern machine learning models when applied to
fluorescence microscopy images. This method includes detection, segmentation,
and tracking elements, with the results demonstrated on a Cell Tracking
Challenge dataset.
