Alia Azadi, Abolfazl Zargari, Avlyn V Singer, Gerrald A Lodewijk, Carly Guiltinan, S Ali Shariati
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引用次数: 0
Abstract
Single-cell quantitative time-lapse microscopy is a valuable tool for directly observing the dynamic activities and decisions of individual stem cells within a population. Successful application of time-lapse microscopy requires automated segmentation of thousands of individual cells to track their features frame-by-frame over time. However, this can be challenging due to unpredictable cell movement, changes in cell shape, and mitotic activity. Such limitations are particularly notable in imaging approaches such as label-free phase-contrast microscopy, which is widely available and non-toxic for long-term time-lapse imaging. Our team has previously developed a deep-learning model and software, DeepSea, to overcome the challenges of single-cell phase-contrast microscopy through automation of segmentation and tracking. In this chapter, we present how to install and use DeepSea to segment and track individual cells in time-lapse phase-contrast images.
期刊介绍：
For over 20 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-by-step fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice.
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