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. 2020 Nov 9;21:509. doi: 10.1186/s12859-020-03809-7

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© The Author(s) 2020

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 16 — Fully automated good top and side-View (square and circular) training particles-selection using AutoCryoPicker [24] approach and KLH dataset [26]. a, d The Ground truth (particles manually labeled) for the different cryo-EM images from the KLH dataset [26]. b, e Side-view particles-selection results using AutoCryoPicker based IBC algorithm [24] and perfect side-view (square) particles-selection algorithm. c, f Top-view particles-selection results using a modified CHT algorithm [24] (the red ‘ + ’ sign is the center of each particle, and blue circles around each particle are the radius of each particle by the blue circle around each particle