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. 2020 Aug 4;19(2):267–284. doi: 10.1007/s12021-020-09483-7

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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 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/.

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Fig. 6 — Quantitative evaluations of different registration methods. Heat map of 140 brain regions or nuclei. The applied metric is Dice scores. The closer a color is to red, the lower the registration accuracy is, and the closer a color is to blue, the higher the registration accuracy is. Quantitative evaluations of Affine (0.493 ± 0.200), LD demons (0.637 ± 0.136), SyN (0.756 ± 0.106), QuickSilver (0.728 ± 0.103), VNet (0.740 ± 0.122) and DeepMapi (0.825 ± 0.081) are presented. Additionally, an asterisk (*) is used to mark the regions with the highest DeepMapi values