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Flow Cytometry

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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Virtual cell imaging: A review on simulation methods employed in image cytometry.

Vladimír Ulman¹, David Svoboda¹, Matti Nykter²

¹Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|December 7, 2016

Summary

This summary is machine-generated.

Simulated cell and microscope images aid cytometry algorithm development and cell biology research. This review details advances in digital cell generation and image simulation for various applications.

Keywords:

cell imaging cell model digital cell digital phantom ground truth image cytometry simulation validation virtual imaging

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Area of Science:

Biomedical Imaging
Computational Biology
Cytometry

Background:

Simulated cell and microscope images are crucial for developing and validating image analysis algorithms in cytometry.
Current simulation methods range from simple models of cell components to complex, learning-based scene synthesis.

Purpose of the Study:

To review advances in artificial digital cell generation and microscope image simulation.
To provide examples of simulated image applications in cell biology.
To discuss the validation and future of simulation-based approaches.

Main Methods:

Review of artificial digital cell generation techniques, from particle to tissue scales.
Examination of microscope image simulation methods.
Analysis of validation strategies for simulated biological images.

Main Results:

Significant progress in generating diverse and complex simulated cell images.
Demonstrated utility of simulated images in tasks like subcellular object detection and cell tracking.
Established methods for validating simulation approaches against real-world data.

Conclusions:

Simulation is a powerful tool for advancing cytometry and cell biology research.
Future possibilities include more sophisticated simulations and broader applications.
Limitations in current simulation fidelity and validation need to be addressed.