Manuscript received August 17, 2022; revised October 7, 2022; accepted November 11, 2022.
Abstract—Since the dynamics of immune cells change about
phenomena in a living body, it is very important to observe and
analyze cell dynamics in vivo in real-time. For this purpose, it is
necessary to extract the information for the analysis by
accurately tracking individual cells. As a method for this,
general object tracking algorithms based on CNN
(Convolutional Neural Networks) have been actively studied in
the field of computer vision. However, in cell tracking, there are
a large number of cells in fluorescent images that are similar in
color and shape. It is not easy to recognize individual cells once
they are lost due to overlap with other cells. Thus it is difficult
to generate a large amount of training data with correct tracking
trajectories. To cope with the problem of insufficient training
data of cell images, our method extends the data by image
processing and by assigning pseudo-labels. Furthermore, to
obtain information more suitable for dynamic analysis, we
propose to apply the re-identification function based on
Euclidean distance. We demonstrate the effectiveness of our
method with application to time-lapse images of immune cells
against multiple inflammatory stimulations.
Index Terms—Cell tracking, bioimaging, deep learning, cell image analysis
A. Watanabe, K. Fujimoto, H. Shigeta, S. Seno, Y. Uchida, M. Ishii, and H. Matsuda are with Osaka University, Suita, Osaka 565-0871 Japan.
Cite: Aya Watanabe, Kenji Fujimoto, Hironori Shigeta, Shigeto Seno, Yutaka Uchida, Masaru Ishii, and Hideo Matsuda*, "A Cell Tracking Method for Dynamic Analysis of Immune Cells Based on Deep Learning," International Journal of Machine Learning vol. 13, no. 2, pp. 64-69, 2023.Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).