Dataset Information

YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells.

ABSTRACT:

Summary

We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines.

Availability and implementation

YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Lu AX

PROVIDER: S-EPMC6821424 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells.

Lu Alex X AX Zarin Taraneh T Hsu Ian S IS Moses Alan M AM

Bioinformatics (Oxford, England) 20191101 21

<h4>Summary</h4>We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines.<h4>Availability and implementation</h4>YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation.<h4>Supplementary information ...[more]

PMID: 31095270

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Project description:Localization-based superresolution microscopy techniques such as Photoactivated Localization Microscopy (PALM) and Stochastic Optical Reconstruction Microscopy (STORM) have allowed investigations of cellular structures with unprecedented optical resolutions. One major obstacle to interpreting superresolution images, however, is the overcounting of molecule numbers caused by fluorophore photoblinking. Using both experimental and simulated images, we determined the effects of photoblinking on the accurate reconstruction of superresolution images and on quantitative measurements of structural dimension and molecule density made from those images. We found that structural dimension and relative density measurements can be made reliably from images that contain photoblinking-related overcounting, but accurate absolute density measurements, and consequently faithful representations of molecule counts and positions in cellular structures, require the application of a clustering algorithm to group localizations that originate from the same molecule. We analyzed how applying a simple algorithm with different clustering thresholds (t(Thresh) and d(Thresh)) affects the accuracy of reconstructed images, and developed an easy method to select optimal thresholds. We also identified an empirical criterion to evaluate whether an imaging condition is appropriate for accurate superresolution image reconstruction with the clustering algorithm. Both the threshold selection method and imaging condition criterion are easy to implement within existing PALM clustering algorithms and experimental conditions. The main advantage of our method is that it generates a superresolution image and molecule position list that faithfully represents molecule counts and positions within a cellular structure, rather than only summarizing structural properties into ensemble parameters. This feature makes it particularly useful for cellular structures of heterogeneous densities and irregular geometries, and allows a variety of quantitative measurements tailored to specific needs of different biological systems.

Dataset Information

YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells.

Summary

Availability and implementation

Supplementary information

Publications

YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells.

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