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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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Automated Cell Selection Using Support Vector Machine for Application to Spectral Nanocytology.

Qin Miao¹, Justin Derbas², Aya Eid¹

¹Biomedical Engineering Department, Northwestern University, Evanston, IL 60208, USA.

Biomed Research International

|February 24, 2016

Summary

Automated cell selection using Support Vector Machine (SVM) classifiers improves diagnostic accuracy for premalignant tumors. This method efficiently removes corrupted cells from smear images, reducing manual labor and bias in nanoscale analysis.

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Area of Science:

Nanotechnology
Biomedical Engineering
Computational Biology

Background:

Partial wave spectroscopy (PWS) quantifies nanoscale cell structures for detecting premalignant tumors.
Accurate cell selection from smear images is crucial for PWS analysis but is currently manual, leading to inefficiencies and variability.
Manual cell selection is time-consuming, labor-intensive, prone to bias, and suffers from inter- and intraoperator variability.

Purpose of the Study:

To develop an automated classification scheme for identifying and removing non-diagnostic cells and debris from raw smear images.
To enhance the efficiency and reliability of cell selection for PWS analysis in cancer diagnostics.
To reduce subjectivity and improve consistency in the analysis of smear samples.

Main Methods:

Digitization of smear samples through low-magnification transmission imaging and image stitching.
Object extraction from digitized images using segmentation algorithms.
Development of a Support Vector Machine (SVM) classifier trained on a manually curated dataset of cell features to distinguish between suitable and unsuitable cells.

Main Results:

The developed classification scheme successfully identifies and removes corrupted cells and debris.
The automated selection algorithm achieved a high sensitivity of 95%.
The algorithm demonstrated a low error rate of 93% in cell selection.

Conclusions:

Automated cell selection using SVM classifiers significantly improves the process of preparing smear images for PWS analysis.
This approach offers a more objective, efficient, and reproducible method for cell selection compared to manual methods.
The findings suggest a potential for enhanced early cancer detection through improved nanoscale analysis of easily accessible tissues.