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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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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

Systematic evaluation of computational methods for cell segmentation.

Rongrong Yang¹, Guangfu Xue¹, Zuxiang Wang²

¹Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, 92 Xidazhi Street, Nangang District, Harbin 150001, Heilongjiang Province, China.

Briefings in Bioinformatics

|February 24, 2026

Summary

This summary is machine-generated.

Deep learning significantly improves cell segmentation, especially when combining image and sequencing data. Our framework classifies methods by task and data type, offering a comprehensive performance benchmark.

Keywords:

cell segmentation deep learning image processing nuclei segmentation spatial transcriptome

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Last Updated: May 4, 2026

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Published on: August 13, 2014

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Area of Science:

Computational Biology
Bioimaging Analysis
Machine Learning in Life Sciences

Background:

Cell segmentation is vital for understanding cell biology, disease mechanisms, and diagnostics.
Existing reviews categorize methods by technical evolution, not fully capturing deep learning's impact.
Current evaluations often neglect multimodal data's potential for improving segmentation.

Purpose of the Study:

To propose a dual-dimensional classification framework for deep learning-based cell segmentation methods.
To systematically review and categorize methods based on task (semantic/instance) and data (single/multimodal).
To establish a benchmark for evaluating segmentation algorithms using diverse datasets and modalities.

Main Methods:

Developed a dual-dimensional classification framework: task-oriented and data-oriented.
Conducted a systematic review and classification of deep learning segmentation methods.
Created a benchmark test using five datasets (microscopy and integrated sequencing-imaging data).
Assessed seven algorithms on effectiveness, robustness, and efficiency.

Main Results:

Deep learning models generally outperform traditional cell segmentation algorithms.
The performance advantage of deep learning is amplified with multimodal data, particularly integrating sequencing information.
The proposed framework provides a structured approach to understanding and evaluating cell segmentation techniques.

Conclusions:

Deep learning, especially with multimodal data integration, represents a significant advancement in cell segmentation.
The dual-dimensional classification and benchmark offer valuable insights for method selection and development.
Future research should leverage multimodal data for enhanced accuracy and robustness in cell analysis.