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. 2018 Aug 14;8:12108. doi: 10.1038/s41598-018-30557-9

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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(A) Foci counting results for a representative pancreatic tissue image using: manual foci detection, with the proposed algorithm of this paper, and a published algorithm called Find Foci. (B) The foci counts for ten test pancreatic images counted by: the proposed algorithm, two manual raters (the training rater and an independent rater), and Find-foci. The agreement between the two manual raters and the proposed algorithm is considerably higher compared to that of with Find-foci. (C) The accuracy of the proposed algorithm and Find-foci for the same ten images as in ‘B’. Here the training rater’s foci locations were treated as the ground truth. The proposed algorithm’s average accuracy was ~77% while Find Foci’s was ~45%. (D) Bland-Altman plot of the difference vs. mean for counts: between the proposed algorithm vs. the training rater; between the independent rater vs. the training rater; and between Find-foci vs. the training rater. Shown by dotted lines are the respective 95% Limits of Agreements (LOA).