瑞典Rickard Sjögren研究小组揭示一个无需标记活细胞分割的大规模数据集-LIVECell。该研究于2021年8月30日发表于国际学术期刊《自然-方法学》杂志。

研究人员展示了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，由具有不同细胞形态和培养密度的160多万个细胞组成。为了进一步证明其用途，研究人员使用LIVECell处理了基于卷积神经网络的模型，并利用一套基准评估模型测试了分割的准确性。

据了解，光学显微镜与成熟二维细胞培养方法相结合，有助于利用高通量定量成像研究生物现象。对图像中的单个细胞进行准确分割能够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方法。基于深度学习的图像分割方法被认为是最先进的图像分割方法，但通常需要大量带注释的数据，在无标记细胞成像领域没有合适的可用资源。

附：英文原文

Title: LIVECell—A large-scale dataset for label-free live cell segmentation

Author: Edlund, Christoffer, Jackson, Timothy R., Khalid, Nabeel, Bevan, Nicola, Dale, Timothy, Dengel, Andreas, Ahmed, Sheraz, Trygg, Johan, Sjgren, Rickard

Issue&Volume: 2021-08-30

Abstract: 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. The LIVECell dataset comprises annotated phase-contrast images of over 1.6 million cells from different cell lines during growth from sparse seeding to confluence for improved training of deep learning-based models of image segmentation.

DOI: 10.1038/s41592-021-01249-6

Source: https://www.nature.com/articles/s41592-021-01249-6