Gravitational cell detection and tracking in fluorescence microscopy data

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

Základní údaje

• Originální název: Gravitational cell detection and tracking in fluorescence microscopy data
• Název anglicky: Gravitational cell detection and tracking in fluorescence microscopy data
• Autoři: EFTIMIU, Nikomidisz Jorgosz a Michal KOZUBEK.
• Vydání: IEEE International Symposium on Biomedical Imaging 2024, 2024.
• Nakladatel: IEEE

Další údaje

• Typ výsledku: Stať ve sborníku
• Utajení: není předmětem státního či obchodního tajemství
• Organizační jednotka: Fakulta informatiky
• Klíčová slova anglicky: Image analysis, cell detection, cell tracking, Cell Tracking Challenge
• Štítky: cbia-web

Anotace

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.

Anotace anglicky

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.