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Updated: Aug 9, 2025

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

Published on: January 12, 2018

Data for assessing red blood cell deformability from microscopy images using deep learning.

Erik S Lamoureux^1,2, Emel Islamzada^2,3, Matthew V J Wiens⁴

¹Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada.

|February 17, 2023

Summary

This summary is machine-generated.

Measuring red blood cell (RBC) deformability from microscopy images can assess RBC quality for transfusions. This dataset enables AI development for automated RBC deformability analysis.

Keywords:

Computer vision Dataset Deep learning Deformability Machine learning Microfluidics Optical microscopy Red blood cells

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Last Updated: Aug 9, 2025

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

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Published on: January 12, 2018

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Area of Science:

Biophysics
Cell Biology
Biomedical Engineering

Background:

Red blood cell (RBC) deformability is crucial for microvascular circulation.
Deformability varies between individuals and declines with aging, disease, and storage.
RBC deformability is a potential biomarker for blood transfusion quality assessment.

Purpose of the Study:

To create a dataset of live cell microscopy images of RBCs.
To enable the development of deep learning algorithms for RBC deformability assessment.
To facilitate automated and standardized measurement of RBC quality.

Main Methods:

Collected live cell microscopy images of RBC samples from 10 healthy donors.
Sorted RBCs into fractions based on deformability using a microfluidic ratchet device.
Imaged sorted RBC fractions using a Nikon Ti-2E inverted microscope with specific objectives and camera.

Main Results:

Generated a dataset of high-resolution microscopy images linked to specific RBC deformability fractions.
The dataset provides a basis for training machine learning models.
The data facilitates the association of visual cell characteristics with functional deformability.

Conclusions:

The developed dataset supports the creation of deep learning models for RBC deformability analysis.
Automated image-based assessment can improve RBC quality control for transfusions.
This approach offers a standardized and efficient method for evaluating RBC health.