Gravitational cell detection and tracking in fluorescence microscopy data

EFTIMIU, Nikomidisz Jorgosz and Michal KOZUBEK. Gravitational cell detection and tracking in fluorescence microscopy data. In IEEE International Symposium on Biomedical Imaging 2024. IEEE, 2024.

Basic information

• Original name: Gravitational cell detection and tracking in fluorescence microscopy data
• Name (in English): Gravitational cell detection and tracking in fluorescence microscopy data
• Authors: EFTIMIU, Nikomidisz Jorgosz and Michal KOZUBEK.
• Edition: IEEE International Symposium on Biomedical Imaging 2024, 2024.
• Publisher: IEEE

Other information

• Type of outcome: Proceedings paper
• Confidentiality degree: is not subject to a state or trade secret
• Organization unit: Faculty of Informatics
• Keywords in English: Image analysis, cell detection, cell tracking, Cell Tracking Challenge
• Tags: cbia-web

Abstract

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.

Abstract (in English)

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.