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Related Experiment Video

Updated: May 13, 2026

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Published on: July 18, 2013

An algorithm to automate yeast segmentation and tracking.

Andreas Doncic¹, Umut Eser, Oguzhan Atay

¹Department of Biology, Stanford University, Stanford, California, United States of America.

|March 23, 2013

Summary

This summary is machine-generated.

This study introduces an automated algorithm for segmenting and tracking budding yeast cells. The method uses yeast-specific traits for robust cell analysis, improving microscopy data interpretation.

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Area of Science:

Cellular biology
Microscopy techniques
Computational biology

Background:

Quantitative fluorescence microscopy advances cellular process understanding.
Single-cell imaging reveals phenomena like cell-to-cell variability, difficult to detect in population studies.
Accurate cell segmentation and tracking are crucial but challenging for analyzing dynamic cellular behaviors.

Purpose of the Study:

To develop a fully automated algorithm for segmenting and tracking budding yeast cells in colonies.
To overcome limitations in current cell segmentation and tracking methods.
To enable more robust analysis of dynamic cellular processes.

Main Methods:

Developed an algorithm for automated segmentation and tracking of budding yeast cells.
Incorporated yeast-specific traits (immobility, growth rate) into the segmentation process.
Utilized a set of threshold values, rather than a single optimized threshold, for robust image segmentation.

Main Results:

Achieved fully automated segmentation and tracking of budding yeast cells.
Demonstrated robust segmentation by using a range of threshold values.
The algorithm effectively handles the challenges of segmenting and tracking cells in growing colonies.

Conclusions:

The presented algorithm provides an efficient and robust solution for budding yeast cell segmentation and tracking.
This tool can significantly accelerate experimental pipelines relying on single-cell analysis.
Enhances the study of dynamic cellular processes and cell-to-cell variability.