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. 2022 Aug 5;12:13460. doi: 10.1038/s41598-022-17341-6

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© The Author(s) 2022, corrected publication 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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The boxplots of segmentation performance results of the (a) Dice similarity coefficient score (DSC), (b) Jaccard index (JI), (c) precision (PR), (d) recall (RC), (e) relative volume difference (RVD), (f) volume of error (VOE), and (g) mean curve distance (MCD) for the deep learning networks, Canal-Net, ConvLTSM 3D U-Net (LSTM 3DU), MPL 3D U-Net (MPL 3DU), 3D U-Net (3DU), SegNet, and 2D U-Net (2DU). Each box contains the first and third quartile of data. The medians are located inside of the boxes, visualized as red lines. The whiskers are extended above and below each box in ± 1.5 times the interquartile range (IQR), and the outliers are visualized as red + marks defining values 1.5 IQR away from the box.