LIVECell

Light microscopy is a cheap, accessible, non-invasive modality that when combined with well-established
protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological
phenomena. Accurate segmentation of individual cells enables exploration of complex biological questions, but
this requires sophisticated imaging processing pipelines due to the low contrast and high object density.

Deep learning-based methods are considered state-of-the-art for most computer vision problems but require vast
amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular
imaging.

To address this gap we present LIVECell, a high-quality, manually annotated and expert-validated dataset
that is the largest of its kind to date, consisting of over 1.6 million cells from a diverse set of cell morphologies
and culture densities. To further demonstrate its utility, we provide convolutional neural network-based models
trained and evaluated on LIVECell.