Project Director

Qin, Hong

Department Examiner

Liang, Yu

Department

Dept. of Computer Science and Engineering

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

The budding yeast Saccharomyces cerevisiae is an effective model for studying cellular aging. We can measure the lifespan of yeast cells in two ways: replicative and chronological lifespans. Chronological focuses on the time that a cell can survive. The replicative lifespan (RLS) is the number of cell divisions that a single mother cell can go through before ceases to be dividing. RLS is a measurement of individual cells and is more informative on the aging process than in chronological lifespan. Many genes that influence yeast RLS have been shown to be highly conserved and have a similar effect on aging in humans. Hence, studies on cellular aging typically focus on RLS. RLS is traditionally measured by micro-dissection – a tedious and time-consuming process. Recently, a high-throughput yeast aging analysis (HYAA) based on microfluidics measurement of replicative aging has been developed. Each mother cell is captured by a trap on the microfluidic device. This device generates an enormous amount of dataset, but the process to manually track these objects is tedious and time consuming and would take years with how large a single dataset can be. This thesis is to address the challenges on how to efficiently and reliably infer the RLS from thousands of time-lapse microscopic images. We implemented two deep learning methods, Faster R-CNN and MASK R-CNN to detect cell the objects. Our results show that Mask R-CNN is a promising method to automate the HYAA image analysis compared to Faster R-CNN approach.

Acknowledgments

Research on cellular aging in Dr. Hong Qin’s research group is funded by NSF Career award 1453078 transferred to 1720215 and NSF BD Spoke award 1761839. I would like to acknowledge Mehran Ghafari, Haobo Guo, Cristian Rudas, and Trevor Peyton for helping on different parts through this research paper.

Degree

B. S.; An honors thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Bachelor of Science.

Date

8-2020

Subject

Cells--Aging; Machine learning

Keyword

Faster R-CNN; Instance segmentation; Machine learning; Mask R-CNN; Object tracking; Replicative lifespan

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Document Type

Theses

Extent

iii, 35 leaves

DCMI Type

Text

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by-nc-nd/4.0/

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