Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. 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. 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. 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. 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.

光学显微镜与完善的二维细胞培养方案相结合，有助于高通量定量成像研究生物现象。图像中单个细胞的准确分割可以探索复杂的生物学问题，但在低对比度和高物体密度的情况下可能需要复杂的成像处理管道。基于深度学习的方法被认为是图像分割的最新技术，但通常需要大量注释数据，而在无标记细胞成像领域没有合适的可用资源。在这里，我们展示了 LIVECell，这是一个大型、高质量、手动注释和专家验证的相差图像数据集，由来自不同细胞形态和培养密度的超过 160 万个细胞组成。