Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

7.8K
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.
7.8K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

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

Molecular biology of the cell·2025
Same author

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

bioRxiv : the preprint server for biology·2025
Same author

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

Current biology : CB·2023
Same author

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

Nature communications·2023
Same author

BubR1 recruitment to the kinetochore via Bub1 enhances spindle assembly checkpoint signaling.

Molecular biology of the cell·2022
Same author

Kre28-Spc105 interaction is essential for Spc105 loading at the kinetochore.

Open biology·2022
Same journal

Editorial: Technologies for RNA Detection.

Bio-protocol·2026
Same journal

One-Step Affinity Purification of MarathonRT Reverse Transcriptase for RNA Sequencing Applications.

Bio-protocol·2026
Same journal

Enhanced RNA-Seq Expression Profiling and Functional Enrichment in Non-model Organisms Using Custom Annotations.

Bio-protocol·2026
Same journal

Using Combined Fluorescent In Situ Hybridization With Immunohistochemistry to Co-localize mRNA in Diverse Neuronal Cell Types.

Bio-protocol·2026
Same journal

Stepwise Protocol for Alternative Splicing Analysis in Single-Cell SMART-Seq2 RNA-Seq Data.

Bio-protocol·2026
Same journal

Enriching Bacteria-Specific RNA From Host Samples Before NGS With Transcript-Capture.

Bio-protocol·2026
查看所有相关文章

相关实验视频

Updated: Mar 3, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

13.5K

如何使用Cell-APP训练自定义细胞细分模型

Anish J Virdi1, Ajit P Joglekar1,2

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

Bio-protocol
|March 2, 2026
PubMed
概括
此摘要是机器生成的。

细胞APP自动创建用于细胞细分模型的训练数据,使用配对传输光和光图像. 这通过减少手动注释时间来加速细胞生物学发现.

关键词:
细胞细分 细胞细分计算机视觉 计算机视觉 计算机视觉数据集注释数据集注释深度学习是一种深度学习.高通量显微镜的使用.

更多相关视频

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

648
Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
07:42

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

Published on: February 7, 2021

6.0K

相关实验视频

Last Updated: Mar 3, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

13.5K
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

648
Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
07:42

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

Published on: February 7, 2021

6.0K

科学领域:

  • 细胞生物学 细胞生物学
  • 显微镜的使用方法
  • 深度学习是一种深度学习.

背景情况:

  • 对于细胞细分的深度学习模型需要大量的注释数据.
  • 手动注释显微镜数据是耗时的,限制了模型的开发.
  • 现有的方法在传输光图像的高效注释方面扎.

研究的目的:

  • 开发一个自动化工具,Cell-APP,用于对传输光细胞细分的训练数据进行注释.
  • 为了使高性能细胞细分模型可以在减少手工劳动的情况下进行训练.
  • 根据提取的光特征来促进细胞的分类.

主要方法:

  • 细胞APP使用配对传输光 (TL) 和光图像作为输入.
  • 细胞位置从光图像中提取,并用于提示μSAM模型.
  • μSAM 在相应的TL图像上生成细胞面具.
  • 可选:从光图像中提取单细胞特征,进行无监督分类.

主要成果:

  • 细胞APP成功地自动化了TL细胞细分的训练数据的注释.
  • 经过训练的模型可以证明各种细胞系 (HeLa,U2OS,HT1080,RPE-1) 的准确细分和细胞周期标记.
  • 生成的注释支持长期跟踪应用程序的一致细分.

结论:

  • 细胞APP显著减少了细胞生物学中手动数据注释的瓶.
  • 该工具可以开发强大的深度学习模型,用于细胞细分和分析.
  • 通过Python包索引,可以通过用户友好的GUI访问Cell-APP.