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. 2020 Nov 30;11:128. doi: 10.1186/s13244-020-00946-8

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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/.

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Fig. 3 — A scheme of this study, with MRI images and the segmentation results of an example from Dataset 1 in the testing set. a–c the original water image, fat image and PDFF map provided by Dataset 1, d the manual segmentation masks of four ROIs, e, f the preprocessed water image and fat image covering the whole thigh muscles as CNN input channels, g the automated CNN-derived segmentation masks. For d, g, segmentations are superimposed on the water image, with ROI1 (quadriceps femoris) in red, ROI2 (sartorius) in green, ROI3 (gracilis) in blue and ROI4 (hamstrings) in yellow