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Updated: Sep 25, 2025

Analyzing Platelet Subpopulations by Multi-color Flow Cytometry

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Using DeepLab v3 + -based semantic segmentation to evaluate platelet activation.

Tsung-Chen Kuo¹, Ting-Wei Cheng¹, Ching-Kai Lin^1,2,3

¹Department of Mechanical Engineering, College of Engineering, National Chiao Tung University, Hsin-Chu, Taiwan.

Medical & Biological Engineering & Computing

|April 29, 2022

Summary

This summary is machine-generated.

This study introduces a DeepLab v3+ model for automated platelet activation analysis using SEM images. The AI model accurately counts platelets, saving time and labor in research.

Keywords:

Activation process Automatic counting Deep learning Platelet Semantic segmentation

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Area of Science:

Biomedical Engineering
Computational Biology
Hematology

Background:

Manual counting of activated platelets from SEM images is time-consuming and labor-intensive.
Existing automated methods like flow cytometry struggle with the complex morphology of activated platelets.
Current image analysis techniques lack precision in recognizing transformed platelets.

Purpose of the Study:

To develop an automated method for evaluating platelet activation and counting platelets using semantic segmentation.
To improve the accuracy and efficiency of platelet analysis from scanning electron microscopy (SEM) images.

Main Methods:

Utilized DeepLab v3+ semantic segmentation model for image analysis.
Incorporated deformable convolution, a pretrained model, and deep supervision for enhanced feature extraction and accuracy.
Calculated the number of activated platelets by dividing the segmented platelet area by the average platelet area.

Main Results:

The DeepLab v3+ model successfully counted activated platelets at various stages from SEM images.
Achieved an overall error rate below 20% in platelet counting.
Demonstrated a reduced error rate of approximately 10% for specific activation stages (2 and 4).

Conclusions:

The proposed AI-driven approach offers a significant improvement in labor and time efficiency for platelet activation evaluation.
This method facilitates further research in hematology and related fields by providing accurate and automated platelet analysis.
The study highlights the potential of advanced deep learning models in analyzing complex biological images.