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.

翻译：荧光显微图像中细胞的自动检测与跟踪是计算机视觉技术在生物医学研究与临床实践中的重要应用。尽管机器学习方法在该领域日益普及，但经典算法在这两项任务中仍具有显著优势，包括更强的可解释性、更快的计算速度、更低的硬件要求以及更稳定的性能。本文提出一种基于引力场的新方法，该方法能够与当代机器学习模型相抗衡，并在应用于荧光显微图像时可能实现更优性能。本方法融合了检测、分割与跟踪等要素，并在细胞追踪挑战赛数据集上展示了实验结果。