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新方法利用大规模数据注释和深度学习对组织图像进行具有人类水平的全细胞分割
2021-11-22 15:14

美国加州理工学院David Van Valen、斯坦福大学Michael Angelo等研究人员合作利用大规模数据注释和深度学习对组织图像进行具有人类水平的全细胞分割。该项研究成果于2021年11月18日在线发表在《自然—生物技术》杂志上。

研究人员表示,组织成像数据分析的一个主要挑战是细胞分割——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,研究人员构建了TissueNet,这是一个用于训练分割模型的数据集,它包含了超过100万个手动标记的细胞,比之前发布的所有分割训练数据集多了一个数量级。研究人员使用TissueNet来训练了Mesmer,这是一种支持深度学习的分割算法。研究人员证明了Mesmer比以前的方法更准确,能够适用于TissueNet中所有的组织类型和成像平台,并且达到了人类水平的表现。

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,这在以前的方法中是具有挑战性的。然后,研究人员对Mesmer进行了调整,以利用高度复用数据集中的细胞系信息,并使用这一增强版来量化人类妊娠期的细胞形态变化。所有代码、数据和模型都已作为公开资源发布。

附:英文原文

Title: Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Author: 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

Issue&Volume: 2021-11-18

Abstract: A principal 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 1million 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.

DOI: 10.1038/s41587-021-01094-0

Source: https://www.nature.com/articles/s41587-021-01094-0

Nature Biotechnology:《自然—生物技术》,创刊于1996年。隶属于施普林格·自然出版集团,最新IF:68.164
官方网址:https://www.nature.com/nbt/
投稿链接:https://mts-nbt.nature.com/cgi-bin/main.plex


本期文章:《自然—生物技术》:Online/在线发表

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