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. 2023 Oct 13;10:1167500. doi: 10.3389/fcvm.2023.1167500

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© 2023 Tayebi Arasteh, Romanowicz, Pace, Golland, Powell, Maier, Truhn, Brosch, Weese, Lotfinia, van der Geest and Moghari.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Schematic representation of the modified 3D U-Net (37,38) architecture used in this study for segmentation. Up-sampling is performed through nearest-neighbor interpolation. Each arrow denotes a 3 × 3 × 3 convolutional layer, subsequently followed by a rectified linear unit (ReLU) and batch normalization (BN). The channel numbers are mentioned on top of the feature maps. The concluding layer employs a 1 × 1 × 1 convolution to streamline to 9 output channels. These channels are then subjected to voxel-wise classification via a SoftMax layer.