Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.
Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.
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深度学习自动化活细胞成像实验中单个细胞的定量分析
DOI：
10.1371/journal.pcbi.1005177
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发表时间：
2016-11
影响因子：
4.3
通讯作者：
Covert MW
中科院分区：
文献类型：
--
作者：
Van Valen DA;Kudo T;Lane KM;Macklin DN;Quach NT;DeFelice MM;Maayan I;Tanouchi Y;Ashley EA;Covert MW
活细胞成像为研究细胞异质性在动态生命系统中的作用打开了一扇令人兴奋的窗口。这类实验的一个主要的关键挑战是图像分割问题，或确定显微镜图像的哪个部分对应于哪个单个细胞。目前的方法需要许多小时的人工管理，并且依赖于难以在实验室之间共享的方法。它们也不能牢固地分割哺乳动物细胞的细胞质。在这里，我们展示了深度卷积神经网络，一种监督机器学习方法，可以解决生命领域多种细胞类型的这一挑战。我们证明，这种方法可以在不需要荧光细胞质标记的情况下，从相衬图像中稳健地分割细胞核的荧光图像以及单个细菌和哺乳动物细胞的细胞质的相图像。这些网络还可以同时分割和鉴定在共培养中生长的不同哺乳动物细胞类型。与先前方法的定量比较表明，卷积神经网络提高了准确性，并显著减少了管理时间。我们介绍了我们在设计和优化深度卷积神经网络方面的经验，并概述了我们发现的几个设计规则，这些规则导致了强大的性能。我们得出结论，深度卷积神经网络是一种精确的方法，需要较少的培养时间，可推广到多种细胞类型，从细菌到哺乳动物细胞，并将活细胞成像能力扩展到包括多细胞类型系统。动态活细胞成像实验是一个强大的工具，询问生物系统与单细胞分辨率。分析这些测量产生的数据的关键障碍是图像分割，即识别图像的哪些部分属于哪个单独的细胞。在这里，我们表明深度学习是解决这些实验问题的自然技术。我们表明，深度学习更准确，需要更少的时间来整理分割结果，可以分割多种细胞类型，并且可以区分同一图像中存在的不同细胞系。我们强调了特定的设计规则，使我们能够在少量手动注释的图像（约100个细胞）下实现高分割精度。我们期望我们的工作将使以前不可能的新实验成为可能，并减少新实验室加入活细胞成像领域的计算障碍。
Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems. Dynamic live-cell imaging experiments are a powerful tool to interrogate biological systems with single cell resolution. The key barrier to analyzing data generated by these measurements is image segmentation—identifying which parts of an image belong to which individual cells. Here we show that deep learning is a natural technology to solve this problem for these experiments. We show that deep learning is more accurate, requires less time to curate segmentation results, can segment multiple cell types, and can distinguish between different cell lines present in the same image. We highlight specific design rules that enable us to achieve high segmentation accuracy even with a small number of manually annotated images (~100 cells). We expect that our work will enable new experiments that were previously impossible, as well as reduce the computational barrier for new labs to join the live-cell imaging space.
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影响因子：
7.9
作者：
Hughey, Jacob J.;Lee, Timothy K.;Covert, Markus W.
通讯作者：
Covert, Markus W.
影响因子：
3
作者：
Bajcsy P;Cardone A;Chalfoun J;Halter M;Juba D;Kociolek M;Majurski M;Peskin A;Simon C;Simon M;Vandecreme A;Brady M
通讯作者：
Brady M
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
3.7
作者：
Gudla, Prabhakar R.;Nandy, K.;Lockett, S. J.
通讯作者：
Lockett, S. J.
通过巨噬细胞对沙门氏菌的内在化会导致形成非复制的持久性。
DOI：
10.1126/science.1244705
发表时间：
2014-01-10
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Helaine S;Cheverton AM;Watson KG;Faure LM;Matthews SA;Holden DW
通讯作者：
Holden DW