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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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Workflow of our study of automated MRI liver and vessels segmentation with a convolutional neural network (nnU-Net) on non-contrast T1 vibe Dixon acquisitions. The MRI sequences were extracted from a pre-existing picture archiving and communication system (PACS) and manually labelled slice-by-slice. Segmentation experiments with single-modal and multi-modal inputs were defined. In-phase (In), water (W), and opposed-phase (Opp) constituted single-modal inputs. In-phase, water (In-W); in-phase, opposed-phase (In-Opp); in-phase, opposed-phase, water (In-Opp-W); and in-phase, opposed-phase, fat, water (In-Opp-F-W) constituted multi-modal inputs. For each experiment, the nnU-Net was trained and evaluated separately. Lastly, the segmentation results were analyzed quantitatively and statistically compared.