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. 2021 Dec 20;11:24237. doi: 10.1038/s41598-021-03542-y

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© The Author(s) 2021

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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Overview of the segmentation framework for high-resolution synchrotron radiation microtomograms. Top shows the full segmentation pipeline: 1. conversion of 3D tomograms into 2D slices. 2. processing of slicing sets by our model. 3. soft voting is used to fuse the multi-axes prediction into the final segmentation. Top right: 3D rendering of the resulting segmentation (created with 3D Slicer, v4.11, https://www.slicer.org/). Bottom shows our final U-net model architecture and the layer legend (created with Net2Vis³⁸, https://github.com/viscom-ulm/Net2Vis).