Deep-Learning, Label-Free, Quantitative Cell Segmentation with IncuCyte

The Life Science Group Sartorius today announced publication of an article in Nature Methods describing the company’s LIVECell (Label-free In Vitro image Examples of Cells) deep-learning dataset for label-free, quantitative segmentation of live cell images. The open-source dataset includes 5000 label-free phase contrast microscopy images consisting of more than 1.6 million cells of eight cell types with distinct morphologies that have been manually annotated. The set of images includes cells grown from initial seeding densities to fully confluent monolayers, resulting in a large variation in cell size and shape.

“The ability to derive physiologically relevant data from label-free microscopy images is a cornerstone of pharmaceutical research and datasets containing images of millions of cells facilitate exploration of biological phenomena with great statistical power,” said Rickard Sjögren, PhD, Senior Scientist, Sartorius Corporate Research. “To compensate for a lack of image resolution, however, sophisticated imaging processing pipelines are necessary to generate the accurate cell-by-cell, pixel-by-pixel segmentations necessary to capture subtle changes in cell size, shape and texture, particularly if the goal is to investigate events at the level of cellular subpopulations or individual cells.”

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