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相关概念视频

Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

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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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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
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对细胞细分的计算方法进行系统的评估.

Yuxing Wang1,2, Junhan Zhao3,4, Hongye Xu1

  • 1Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA.

bioRxiv : the preprint server for biology
|February 14, 2024
PubMed
概括
此摘要是机器生成的。

这项研究评估了18种用于生物医学图像的细胞细分方法. 基于注意力的方法表现最好,并提供了选择最佳细胞细分工具的指导方针.

关键词:
一个基准的基准.细胞细分 细胞细分深度学习是一种深度学习.图像成像技术可以提供图像.

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Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone
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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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科学领域:

  • 生物医学图像分析
  • 计算生物学是一种计算生物学.
  • 细胞成像 细胞成像

背景情况:

  • 细胞细分对于生物医学图像分析至关重要.
  • 现有的计算方法的性能在各种场景中并未得到充分理解.
  • 实例细分和细胞核细分是关键的挑战.

研究的目的:

  • 系统地评估18种细胞细分方法.
  • 确定影响细分业绩的因素.
  • 为选择最佳方法提供指导方针,并为预训练模型引入资源.

主要方法:

  • 对18种计算细分方法的评估.
  • 用光显微镜和光染色图像进行细胞核和整个细胞细分的测试.
  • 分析诸如图像通道,训练数据和细胞形态等因素.

主要成果:

  • 具有注意力机制的通用用途方法显示出优异的性能.
  • 分段性能因图像通道,训练数据和细胞形态学而有所不同.
  • 评估了不同图像模式的方法概括性.

结论:

  • 建议基于注意力的方法用于强大的细胞细分.
  • 了解影响因素是优化细分的关键.
  • 塞格尔资源通过提供预训练模型来帮助研究人员,节省时间和精力.