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. 2022 Dec 21;12:22059. doi: 10.1038/s41598-022-26328-2

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© The Author(s) 2022

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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Comparison of liver segmentation performance with different model inputs: (1) single-modal in-phase (In) in the first column, (2) multimodal in-phase and water reconstruction in the second column (In-W), (3) multi-modal in-phase, opposed-phase, fat, and water reconstructions in the third column (In-Opp-F-W) and (4) single-modal opposed-phase (Opp) in the last column. The correct segmentation is shown in red for the liver parenchyma, purple for the portal veins, and blue for the hepatic veins. Under-segmentation is shown in cyan and over-segmentation in yellow, as performed by the model with an in-phase sequence.