Method of Tracking and Analysis of Fluorescent-Labeled Cells Using Automatic Thresholding and Labeling

TL;DR

This work addresses the need for robust, cross-frame cell tracking and quantitative signal analysis in fluorescence-labeled cell images without relying on extensive AI retraining. It introduces a three-stage pipeline: automatic thresholding and labeling for nuclei detection, centroid-and-rectangle–based tracking across frames, and masked-region signal ratio calculation between nucleus and cytoplasm. Through experiments varying binarization opening/closing operations, the paper identifies preprocessing settings that maximize tracking performance while preserving nucleus contours. The approach enables continuous measurement of cytoplasm-nucleus signal dynamics, with potential to accelerate high-throughput drug screening where cell correspondence across frames is essential.

Abstract

High-throughput screening using cell images is an efficient method for screening new candidates for pharmaceutical drugs. To complete the screening process, it is essential to have an efficient process for analyzing cell images. This paper presents a new method for efficiently tracking cells and quantitatively detecting the signal ratio between cytoplasm and nuclei. Existing methods include those that use image processing techniques and those that utilize artificial intelligence (AI). However, these methods do not consider the correspondence of cells between images, or require a significant amount of new learning data to train AI. Therefore, our method uses automatic thresholding and labeling algorithms to compare the position of each cell between images, and continuously measure and analyze the signal ratio of cells. This paper describes the algorithm of our method. Using the method, we experimented to investigate the effect of the number of opening and closing operations during the binarization process on the tracking of the cells. Through the experiment, we determined the appropriate number of opening and closing processes.

Figures (14)

• Figure 1: Flowchart of proposed method
• Figure 2: Video frame with red fluorescence
• Figure 3: Bounding rectangle of a nucleus in previous frame
• Figure 4: Centroid of a nucleus in current frame
• Figure 5: Comparing positions of bounding rectangle and centroid