Advancing precision single-cell analysis of red blood cells through semi-supervised deep learning using database of patients with post-COVID-19 syndrome

Andrey Kurenkov; Aigul Kussanova; Natasha S. Barteneva

doi:10.1117/12.3008410

12 March 2024 Advancing precision single-cell analysis of red blood cells through semi-supervised deep learning using database of patients with post-COVID-19 syndrome

Andrey Kurenkov, Aigul Kussanova, Natasha S. Barteneva

Author Affiliations +

Proceedings Volume 12846, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXII; 1284602 (2024) https://doi.org/10.1117/12.3008410
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

We developed a semi-supervised deep-learning-based system classifying different types of red blood cells (RBCs) images based on their shape, texture, and size. Specifically, pre-training a convolutional neural network was done on over 35,000 brightfield images of RBCs acquired with an imaging flow cytometer from a post-COVID-19 patient cohort. The system utilizes object localization powered by a YOLO-inspired block for cell identification and a de-blurring CNN block based on FocalNet. A series of convolutional and fully connected layers classifies images into side-view, biconcave, elongated, and additional categories for reticulocytes and erythrocytes. Fine-tuning was done using 7,000 manually labeled brightfield images. Consequent evaluation on a test dataset of 3,000 samples yielded an accuracy of 98.2%. This system can be used for other cell analysis tasks, not requiring large fine-tuning datasets while maintaining high efficiency.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Andrey Kurenkov, Aigul Kussanova, and Natasha S. Barteneva "Advancing precision single-cell analysis of red blood cells through semi-supervised deep learning using database of patients with post-COVID-19 syndrome", Proc. SPIE 12846, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXII, 1284602 (12 March 2024); https://doi.org/10.1117/12.3008410

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