Enhanced Cell Tracking Using A GAN-based Super-Resolution Video-to-Video Time-Lapse Microscopy Generative Model.

ABSTRACT: Cells are among the most dynamic entities, constantly undergoing various processes such as growth, division, movement, and interaction with other cells as well as the environment. Time-lapse microscopy is central to capturing these dynamic behaviors, providing detailed temporal and spatial information that allows biologists to observe and analyze cellular activities in real-time. The analysis of time-lapse microscopy data relies on two fundamental tasks: cell segmentation and cell tracking. Integrating deep learning into bioimage analysis has revolutionized cell segmentation, producing models with high precision across a wide range of biological images. However, developing generalizable deep-learning models for tracking cells over time remains challenging due to the scarcity of large, dive

SUBMITTER: Zargari A 

PROVIDER: S-EPMC11195160 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature