eBook: Advances in Live-Cell Biological Image Analysis

Overview

Live-cell imaging and analysis systems should include automated, objective, quantifiable analysis methods to gain the detailed insights your research needs and ensure robust results every time. This eBook explores how advancements in live-cell imaging and analysis technologies enable more objective and quantitative analysis, leading to deeper insights while supporting scientific research at scale. 

These live-cell imaging technologies are generally grounded in image segmentation - the digitalized and automated recognition of individual cells (or rather, cell boundaries).

In morphological analyses, segmentation can facilitate the capture and processing of metrics for cell size, shape and texture. In subpopulation analyses, segmentation helps preserve spatiotemporal associations among cells - including cells that may be labeled in various ways - potentially revealing subtle cell signaling phenomena. Instead of simply aiding identification of cell subsets, segmentation-based analyses may uncover interactions between different cell types.

Image segmentation is compatible with machine learning and artificial intelligence. Large, well-annotated imaging datasets are being used to train segmentation algorithms. Some datasets compile information about labeled or stained cells; others leverage label-free cell imaging experiments. In either case, subjecting live-cell imaging data to “unsupervised” analyses can reveal patterns that humans, being all too susceptible to fatigue and bias, might have missed.

• Document type: eBook
• Page count: 40
• Read time: 1.5 hours

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