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Electronics
Volume 14
Issue 17
10.3390/electronics14173418
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

High-Precision Detection of Cells and Amyloid-β Using Multi-Frame Brightfield Imaging and Quantitative Analysis

by
Mengyu Li
1,2,
Masahiro Kuragano
2,
Stefan Baar
2,
Mana Endo
2,
Kiyotaka Tokuraku
2 and
Shinya Watanabe
2,*
1
Graduate School of Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan
2
Department of Applied Sciences, Muroran Institute of Technology, Muroran, Hokkaido 050-8585, Japan
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(17), 3418; https://doi.org/10.3390/electronics14173418 (registering DOI)
Submission received: 5 July 2025 / Revised: 9 August 2025 / Accepted: 20 August 2025 / Published: 27 August 2025
(This article belongs to the Special Issue Advanced Machine Learning, Pattern Recognition, and Deep Learning Technologies: Methodologies and Applications, 2nd Edition)
Review Reports Versions Notes

Abstract

This study presents a novel method for high-precision detection and quantitative evaluation of the spatial relationship between cells and amyloid-β (Aβ) in time-lapse brightfield microscopy images. Achieving accurate detection of non-fluorescent cells and Aβ deposits requires high-quality video images free from noise, distortion, and frame-to-frame luminance flicker. To this end, we employ a robust preprocessing pipeline that combines multi-frame integration with vignetting correction to enhance image quality and reduce luminance variability across frames. Key preprocessing steps include background correction via two-dimensional polynomial fitting, temporal smoothing of luminance fluctuations, histogram matching for luminance normalization, and dust artifact removal based on intensity thresholds. This enhanced imaging approach enables accurate identification of Aβ aggregates, which typically appear as jelly-like structures and are difficult to detect under standard brightfield conditions. Furthermore, we introduce a quantitative index to assess the spatial relationship between cells and Aβ concentrations, facilitating detailed analysis under varying Aβ levels.
Keywords: time-lapse brightfield microscopy; image preprocessing; amyloid-β detection; multi-frame imaging; quantitative image analysis time-lapse brightfield microscopy; image preprocessing; amyloid-β detection; multi-frame imaging; quantitative image analysis

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MDPI and ACS Style

Li, M.; Kuragano, M.; Baar, S.; Endo, M.; Tokuraku, K.; Watanabe, S. High-Precision Detection of Cells and Amyloid-β Using Multi-Frame Brightfield Imaging and Quantitative Analysis. Electronics 2025, 14, 3418. https://doi.org/10.3390/electronics14173418

AMA Style

Li M, Kuragano M, Baar S, Endo M, Tokuraku K, Watanabe S. High-Precision Detection of Cells and Amyloid-β Using Multi-Frame Brightfield Imaging and Quantitative Analysis. Electronics. 2025; 14(17):3418. https://doi.org/10.3390/electronics14173418

Chicago/Turabian Style

Li, Mengyu, Masahiro Kuragano, Stefan Baar, Mana Endo, Kiyotaka Tokuraku, and Shinya Watanabe. 2025. "High-Precision Detection of Cells and Amyloid-β Using Multi-Frame Brightfield Imaging and Quantitative Analysis" Electronics 14, no. 17: 3418. https://doi.org/10.3390/electronics14173418

APA Style

Li, M., Kuragano, M., Baar, S., Endo, M., Tokuraku, K., & Watanabe, S. (2025). High-Precision Detection of Cells and Amyloid-β Using Multi-Frame Brightfield Imaging and Quantitative Analysis. Electronics, 14(17), 3418. https://doi.org/10.3390/electronics14173418

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