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Title: A Comparison of YOLO and Mask-RCNN for Detecting Cells from Microfluidic Images
High-throughput microfluidics-based assays can potentially increase the speed and quality of yeast replicative lifespan measurements. One major challenge is to efficiently convert large volumes of time-lapse images into quantitative measurements of cellular lifespans. Here, we address this challenge by prototyping an algorithm that can track cellular division events through family trees of cells. We generated a null distribution using single cells inside microfluidic traps. Based on this null distribution, we prototyped a maximum likelihood algorithm for cell tracking between images at different time-points. We inferred cell family trees through a likelihood based trace-back method. The branching patterns of the cell family trees are then used to infer replicative lifespan of the yeast mother cells. The longest branch of a cell family tree represents the full trajectory of a yeast mother cell. The replicative lifespan of this mother cell can be counted as the number of bifurcating branches of this family tree. In addition, we prototyped a different approach based on summing cells area which improved the replicative lifespan estimation significantly. These generic methods have the potential to accelerate the efficiency and expand the range of quantitative measurement of yeast replicative aging experiments.  more » « less
Award ID(s):
1720215 1761839
NSF-PAR ID:
10318951
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) |
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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