Gravitational cell detection and tracking in fluorescence microscopy data
This addresses the need for explainable, efficient, and consistent cell analysis tools in biomedical research and clinical practice, though it appears incremental as it builds on classical algorithms rather than introducing a new paradigm.
The authors tackled the problem of automatic cell detection and tracking in fluorescence microscopy images by introducing a gravitational force field-based approach, which they demonstrated can compete with or outperform modern machine learning models on a Cell Tracking Challenge dataset.
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.