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

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.