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）是一种低成本、易获取且无创的成像模态，与成熟的二维细胞培养（two-dimensional cell culture）实验方案相结合，可实现高通量定量成像以研究生物学现象。准确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，该任务需要精密的图像处理流程。

基于深度学习（Deep learning）的方法在多数计算机视觉（Computer Vision）任务中被认为是当前最优方案，但这类方法需要海量标注数据，而在无标记细胞成像（label-free cellular imaging）领域目前尚无合适的相关数据集资源。

为填补这一空白，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人工标注且经专家验证的数据集，包含超过160万个来自多种细胞形态与培养密度的细胞样本。为进一步验证其应用价值，我们提供了基于卷积神经网络（Convolutional Neural Network）的模型，并在LIVECell上完成了训练与评估。