![]() The source code for the plugins is available on GitHub. The FindFoci plugins are packaged within the GDSC ImageJ plugins and distributed using an ImageJ2/Fiji update site allowing simple install into ImageJ ( ). The FindFoci web page includes a user manual describing all the plugins. All primary image data are contained within the paper and Supporting Information files. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The authors confirm that all data underlying the findings are fully available without restriction. Received: AugAccepted: NovemPublished: December 5, 2014Ĭopyright: © 2014 Herbert et al. PLoS ONE 9(12):Įditor: Michael Lichten, National Cancer Institute, United States of America FindFoci is provided as an open-source plugin for ImageJ.Ĭitation: Herbert AD, Carr AM, Hoffmann E (2014) FindFoci: A Focus Detection Algorithm with Automated Parameter Training That Closely Matches Human Assignments, Reduces Human Inconsistencies and Increases Speed of Analysis. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. ![]() Our analysis thus reveals wide variation in human assignment of foci and their quantification. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. ![]() ![]() To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. ![]()
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