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Related Concept Videos

Overview Of Cell Separation And Isolation01:20

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.
Subcellular Fractionation01:32

Subcellular Fractionation

The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
Differential Centrifugation
Differential centrifugation is...
Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Related Experiment Video

Updated: Jun 6, 2026

Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

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

Published on: April 8, 2015

Segmentation of cell clumps for quantitative analysis.

Simon Li1, Claudia Buehnemann, Bass Hassan

  • 1Department of Engineering, University of Oxford, UK. simon.li@dtc.ox.ac.uk

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary
This summary is machine-generated.

Accurate cell segmentation is crucial for understanding cell biology. This study introduces a novel method using a multi-scale ridge filter and level set segmentation to precisely delineate individual cells within clumps, improving quantitative image analysis.

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Area of Science:

  • Cell Biology
  • Image Analysis
  • Computational Biology

Background:

  • Microscopy generates vast amounts of cell biology data, necessitating automated quantitative analysis tools.
  • Segmenting cell clumps is challenging due to indistinct cell boundaries, leading to data loss or inaccurate measurements.
  • Cellular communication and varied responses within clumps require precise individual cell segmentation.

Purpose of the Study:

  • To develop an automated method for accurate cell segmentation in microscopy images, specifically addressing challenges posed by cell clumps.
  • To improve the quantitative analysis of cell behavior by enabling precise delineation of individual cells within crowded populations.

Main Methods:

  • Employed a multi-scale ridge filter to enhance faint or unclear boundaries between cells in microscopy images.
  • Utilized a multi-phase level set method with a region competition term for robust cell boundary identification.
  • Integrated ridge filter responses into the level set evolution to guide accurate segmentation.

Main Results:

  • The proposed method effectively segments individual cells within challenging cell clumps, overcoming limitations of previous techniques.
  • Enhanced boundary detection significantly improved the accuracy of cell segmentation compared to methods ignoring clumped cells.
  • Quantitative measurements derived from segmented cells are more reliable, preventing averaging artifacts.

Conclusions:

  • This novel segmentation approach provides a vital tool for quantitative cell biology, enabling deeper insights into cellular processes.
  • The method enhances the analysis of complex cellular populations by accurately segmenting cells in clumps.
  • Accurate segmentation of individual cells is essential for understanding cell-environment interactions and responses.