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. 2020 Oct 27;7:371. doi: 10.1038/s41597-020-00706-9

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© The Author(s) 2020

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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Fig. 6 — Steps involved in generating the ground truth segmentation labels for the inner core tissues. The original images pre-processed by employing rolling ball algorithm and bandpass filtering. Next, the adaptive thresholding has been employed to obtain binary images. Furthermore, morphological operations have been performed to refine the cell boundaries and remove the starch granules. By changing f_cl, f_ch and R at pre-processing steps, possible binary images have been generated. Then, the best image has been selected for manual correction.