Hundreds of label-free cells tracking method based on multi-scale region of interest

Abstract

Dielectrophoresis is a technology that uses the electrical properties of cells to control the movement of cells in a non-contact manner. It is important to observe cell movement in order to analyze cell characteristics using DEP technology. We developed an algorithm that can track the movement of hundreds of unlabeled cells by DEP force. The proposed algorithm consists of a cell detection step using a deep learning detection model and a cell tracking step based on a multiscale region of interest. Cell detection and tracking accuracy using Recall, precision, f-measure, and MOTA on a timelapse microscope image dataset has an accuracy of about 97% or more. In conclusion, by developing an automated tool that can perform imaging-based DEP cell analysis, cell tracking algorithms that can track hundreds of cells simultaneously can reduce cell analysis time and labor.