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相关概念视频

Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

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How to Train Custom Cell Segmentation Models Using Cell-APP.

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Cell-APP: A generalizable method for cell annotation and cell-segmentation model training.

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Signaling protein abundance modulates the strength of the spindle assembly checkpoint.

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The structural flexibility of MAD1 facilitates the assembly of the Mitotic Checkpoint Complex.

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相关实验视频

Updated: Jul 8, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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细胞APP:用于细胞注释和细胞细分模型培训的可通用方法.

Anish J Virdi1, Ajit P Joglekar1,2

  • 1Department of Biophysics, University of Michigan, Ann Arbor, MI 48109.

Molecular biology of the cell
|September 24, 2025
PubMed
概括
此摘要是机器生成的。

细胞APP自动化了用于显微镜的细胞细分数据注释. 该工具通过生成训练数据集来加速高吞吐量分析,使定制和一般细胞细分模型成为可能.

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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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科学领域:

  • 生物技术是生物技术.
  • 计算生物学 计算生物学
  • 显微镜的使用方法

背景情况:

  • 深度学习模型加速显微镜数据分析.
  • 手动注释细胞细分数据是耗时的,并限制了数据集规模.
  • 现有的方法与各种细胞类型和条件作斗争.

研究的目的:

  • 开发一种自动化工具,用于生成用于传输光 (TL) 细胞细分的高质量训练数据.
  • 为了能够创建特定细胞系和一般细胞细分模型.
  • 在深度学习模型开发中克服手动注释的局限性.

主要方法:

  • 细胞APP使用配对的传输光 (TL) 和核光图像.
  • 它从光图像中提取细胞位置,以促使深度学习模型用于细胞面具生成.
  • 细胞根据核特征被分为线粒细胞或非线粒细胞.

主要成果:

  • 细胞APP成功地自动化了细胞细分训练数据的注释.
  • 在Cell-APP数据上训练的基于视觉变压器的模型实现了高性能.
  • 展示了细胞系特异性和多细胞系细分模型的创建.

结论:

  • 细胞APP显著降低了生成细胞细分训练数据所需的努力.
  • 该工具使研究人员能够创建定制的细胞细分模型.
  • 细胞APP为研究界开发一般细胞细分模型提供了一个可扩展的方法.