Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.
Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.
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DOI：
10.1038/s41587-021-01094-0
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发表时间：
2022-04
影响因子：
46.9
通讯作者：
Van Valen, David
中科院分区：
文献类型：
--
作者：
Greenwald, Noah F.;Miller, Geneva;Moen, Erick;Kong, Alex;Kagel, Adam;Dougherty, Thomas;Fullaway, Christine Camacho;McIntosh, Brianna J.;Leow, Ke Xuan;Schwartz, Morgan Sarah;Pavelchek, Cole;Cui, Sunny;Camplisson, Isabella;Bar-Tal, Omer;Singh, Jaiveer;Fong, Mara;Chaudhry, Gautam;Abraham, Zion;Moseley, Jackson;Warshawsky, Shiri;Soon, Erin;Greenbaum, Shirley;Risom, Tyler;Hollmann, Travis;Bendall, Sean C.;Keren, Leeat;Graf, William;Angelo, Michael;Van Valen, David
组织成像数据分析的一个主要挑战是细胞分割，即识别图像中每个细胞的精确边界的任务。为了解决这个问题，我们构建了TissueNet，这是一个用于训练分割模型的数据集，其中包含超过100万个手动标记的细胞，比以前发布的所有分割训练数据集都要多一个数量级。我们使用TissueNet来训练Mesmer，这是一种支持深度学习的分割算法。我们证明Mesmer比以前的方法更准确，在TissueNet中推广到组织类型和成像平台的全部多样性，并达到人类水平的性能。Mesmer能够自动提取关键的细胞特征，如蛋白质信号的亚细胞定位，这是以前的方法所面临的挑战。然后，我们调整了Mesmer来利用高度复用的数据集中的细胞谱系信息，并使用这个增强的版本来量化人类妊娠期间的细胞形态变化。所有的代码、数据和模型都作为社区资源发布。
A major challenge in the analysis of tissue imaging data is cell segmentation, the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data, and models are released as a community resource.
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影响因子：
4.6
作者：
Bankhead P;Loughrey MB;Fernández JA;Dombrowski Y;McArt DG;Dunne PD;McQuaid S;Gray RT;Murray LJ;Coleman HG;James JA;Salto-Tellez M;Hamilton PW
通讯作者：
Hamilton PW
影响因子：
64.5
作者：
Goltsev Y;Samusik N;Kennedy-Darling J;Bhate S;Hale M;Vazquez G;Black S;Nolan GP
通讯作者：
Nolan GP
影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
22.7
作者：
Ali, H. Raza;Jackson, Hartland W.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd
影响因子：
48
作者：
Giesen, Charlotte;Wang, Hao A. O.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd