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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: Jul 3, 2025

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

A systematic evaluation of computational methods for cell segmentation.

Yuxing Wang^1,2, Junhan Zhao^3,4, Hongye Xu¹

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

Biorxiv : the Preprint Server for Biology

|February 14, 2024

Summary

This summary is machine-generated.

This study evaluates 18 cell segmentation methods for biomedical images. Attention-based methods performed best, with guidelines provided for selecting optimal cell segmentation tools.

Keywords:

Benchmark Cell segmentation Deep learning Imaging

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Area of Science:

Biomedical image analysis
Computational biology
Cellular imaging

Background:

Cell segmentation is crucial for biomedical image analysis.
Existing computational methods' performance is not well-understood across diverse scenarios.
Instance segmentation and cell nuclei segmentation are key challenges.

Purpose of the Study:

To systematically evaluate 18 cell segmentation methods.
To identify factors influencing segmentation performance.
To provide guidelines for selecting optimal methods and introduce a resource for pre-trained models.

Main Methods:

Evaluation of 18 computational segmentation methods.
Testing on light microscopy and fluorescence staining images for cell nuclei and whole cell segmentation.
Analysis of factors like image channels, training data, and cell morphology.

Main Results:

General-purpose methods with attention mechanisms showed superior performance.
Segmentation performance varied based on image channels, training data, and cell morphology.
Method generalizability across different image modalities was assessed.

Conclusions:

Attention-based methods are recommended for robust cell segmentation.
Understanding influencing factors is key to optimizing segmentation.
The Seggal resource aids researchers by providing pre-trained models, saving time and effort.