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Overview Of Cell Separation And Isolation

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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Updated: Jun 17, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

无监督的宫细胞实例细分方法整合细胞特征.

Yining Xie¹, Jingling Gao², Xueyan Bi³

¹College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China.

Medical & biological engineering & computing

|November 3, 2024

概括

此摘要是机器生成的。

这项研究引入了一种无监督的方法来细分宫细胞,这对于癌症诊断至关重要. 它利用细胞核-细胞质结构生成伪标签,改善模型训练而不需要手动注释.

关键词:

宫细胞细分的部分化热图感知热图的感知多角度协作细分多角度协作细分自相似性地图的自我相似性地图没有监督的实例细分.

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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

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Published on: June 30, 2017

相关实验视频

Last Updated: Jun 17, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

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Published on: April 8, 2015

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

科学领域:

医疗成像医学成像
计算生物学计算生物学
计算机视觉计算机视觉

背景情况:

细胞实例细分对于宫癌诊断至关重要.
培训模型的像素级注释是劳动密集型和耗时的.
有限的注释数据阻碍了有效的模型培训.

研究的目的:

为宫细胞实例细分提出一种无监督的方法.
为了应对医学成像中注释数据不足的挑战.
整合固有的细胞特征以改善细分.

主要方法:

开发了一个双流框架,同时对细胞核和细胞质进行细分.
核细分采用了标准的细胞受限方法.
细胞质细分涉及一种新的多角度协作方法,它结合了自我相似性地图代和自我监督的热图意识细分.
通过融合核和细胞质细分结果,生成了高质量的伪标签.
有损失函数的循环训练策略鼓励发现新的对象面具.

主要成果:

提出的无监督方法在细胞质细分方面取得了显著的结果.
该方法在ISBI上获得了54.32%的调整后雅卡德指数 (AJI),在MS_CellSeg上为44.64%,在Cx22数据集上为66.52%.
性能超过了研究中评估的其他无监督方法.

结论:

无监督方法有效地通过利用核-细胞质结构关系对宫细胞进行细分.
该方法显著减少了对手动像素级注释的依赖.
这种技术为在资源有限的环境中训练强大的细胞细分模型提供了一个有希望的解决方案.