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. <br><br>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. <br><br>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.

光学显微镜（Light microscopy）是一种成本低廉、易于获取且无创的成像模态，当其结合成熟的二维细胞培养方案时，可实现高通量定量成像，用于研究各类生物学现象。精准分割单个细胞可为探索复杂生物学问题提供可能，但由于图像对比度较低且目标细胞密度较高，该任务需要搭建精密的图像处理流程。<br><br>基于深度学习的方法在绝大多数计算机视觉任务中被视为当前最优方案，但这类方法需要海量带标注数据，而无标记细胞成像（label-free cellular imaging）领域目前尚无合适的可用资源。<br><br>为填补这一研究空白，我们推出了LIVECell数据集——这是目前同类数据集中规模最大的高质量手动标注且经专家验证的数据集，涵盖来自多种细胞形态与培养密度的超过160万个细胞。为进一步验证其应用价值，我们还提供了基于卷积神经网络（Convolutional Neural Network, CNN）、在LIVECell上训练并评估的模型。