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. 2018 Jan 19;8:1223. doi: 10.1038/s41598-017-18453-0

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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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Schematic flow chart and the illustrative result of rs-fMRI-based automatic tumor tissue identification. (a) Mean image of a rs-fMRI data; (b) raw 3D T1 image; (c) T1 image co-registered to the mean rs-fMRI image; (d) initial tumor template; (e) the curve of the largest DICI values plotted against different TNCs settings; (f) final tumor-related component with the largest DICI value across all TNCs settings; and (g) a tumor-related component candidate corresponding to the largest DICI value for TNCs = 90 (but not the optimal TNCs).