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

TL;DR

This paper introduces a new cell tracking and analysis method using automatic thresholding and labeling, improving efficiency in high-throughput cell image screening without extensive AI training.

Contribution

It proposes a novel image processing approach that tracks cells and analyzes signal ratios without relying on AI or large training datasets.

Findings

Optimized opening and closing operations improve cell tracking accuracy.

The method effectively compares cell positions across images.

It enables quantitative analysis of cytoplasm and nuclei signal ratios.

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.