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Numerous studies have developed sophisticated label-free imaging technologies, such as quantitative phase imaging 7, but these often require users to have expertise and complex hardware. However, label-free imaging presents unique challenges. Because of this, recent years have seen renewed interest in label-free imaging approaches. Moreover, stable expression of fluorescent proteins requires genetic manipulation, which may not always be possible in more physiologically relevant primary cell types 5 such as patient-derived induced pluripotent stem cells 6.

Fluorescent proteins have been linked to increased cell death 1, reactive oxygen species accumulation and mitotic arrest 2, interruption of critical cell signaling pathways 3 and impairment of actinвЂ“myosin interactions 4. However, mounting evidence indicates fluorescent sensors can alter biological responses by effecting physiological change. Many such segmentation algorithms rely on the presence of a fluorescent label.

These analyses ultimately rely on robust identification and segmentation algorithms, particularly if the goal is to investigate at the level of individual cells. In pharmaceutical research, the ability to quantify such metrics from high-throughput imaging systems can drive drug discovery by facilitating fast compound screening and efficacy testing. In particular, cellular assays are an accessible medium to obtain physiologically relevant data from images, allowing quantification of the effects of interventions on cell count, proliferation, morphology, migration, cellular interactions and when coupled with fluorescence imaging, protein expression dynamics and cellular events, for example cell death.

The low dimensional complexity of 2D cultures readily facilitates experimental methods, including imaging. Two-dimensional (2D) cell monolayer models of mammalian cells are a cornerstone of cellular based research due to well-established and reproducible protocols. Quantitative imaging offers unequaled spatial and temporal resolution when measuring biological phenomena, which has led to its wide use in cell biology and biomedical research. To further demonstrate its use, we train convolutional neural network-based models using LIVECell and evaluate model segmentation accuracy with a proposed a suite of benchmarks. Here, we present LIVECell, a large, high-quality, manually annotated and expert-validated dataset of phase-contrast images, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. Deep learning-based methods are considered state-of-the-art for image segmentation but typically require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. Accurate segmentation of individual cells in images enables exploration of complex biological questions, but can require sophisticated imaging processing pipelines in cases of low contrast and high object density. Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena.

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