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Creating pull request for 10.21105.joss.04691 #3592

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329 changes: 329 additions & 0 deletions joss.04691/10.21105.joss.04691.crossref.xml
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<citation key="russakovsky_imagenet_2015">
<article_title>ImageNet Large Scale Visual Recognition
Challenge</article_title>
<author>Russakovsky</author>
<journal_title>International Journal of Computer
Vision</journal_title>
<issue>3</issue>
<volume>115</volume>
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Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A.,
Bernstein, M., Berg, A. C., &amp; Fei-Fei, L. (2015). ImageNet Large
Scale Visual Recognition Challenge. International Journal of Computer
Vision, 115(3), 211–252.
https://doi.org/10.1007/s11263-015-0816-y</unstructured_citation>
</citation>
<citation key="perslev_one_2019">
<article_title>One Network to Segment Them All: A General,
Lightweight System for Accurate 3D Medical Image
Segmentation</article_title>
<author>Perslev</author>
<journal_title>Medical Image Computing and Computer Assisted
Intervention – MICCAI 2019</journal_title>
<doi>10.1007/978-3-030-32245-8_4</doi>
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<cYear>2019</cYear>
<unstructured_citation>Perslev, M., Dam, E. B., Pai, A.,
&amp; Igel, C. (2019). One Network to Segment Them All: A General,
Lightweight System for Accurate 3D Medical Image Segmentation. In D.
Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P.-T. Yap,
&amp; A. Khan (Eds.), Medical Image Computing and Computer Assisted
Intervention – MICCAI 2019 (pp. 30–38). Springer International
Publishing.
https://doi.org/10.1007/978-3-030-32245-8_4</unstructured_citation>
</citation>
<citation key="buslaev_albumentations_2020">
<article_title>Albumentations: Fast and Flexible Image
Augmentations</article_title>
<author>Buslaev</author>
<journal_title>Information</journal_title>
<issue>2</issue>
<volume>11</volume>
<doi>10.3390/info11020125</doi>
<issn>2078-2489</issn>
<cYear>2020</cYear>
<unstructured_citation>Buslaev, A., Iglovikov, V. I.,
Khvedchenya, E., Parinov, A., Druzhinin, M., &amp; Kalinin, A. A.
(2020). Albumentations: Fast and Flexible Image Augmentations.
Information, 11(2).
https://doi.org/10.3390/info11020125</unstructured_citation>
</citation>
<citation key="alvarez-borges_u-net_2022">
<article_title>U-Net Segmentation Methods for
Variable-Contrast XCT Images of Methane-Bearing Sand Using Small
Training Datasets</article_title>
<author>Alvarez-Borges</author>
<doi>10.1002/essoar.10506807.2</doi>
<cYear>2022</cYear>
<unstructured_citation>Alvarez-Borges, F. J., King, O. N.
F., Madhusudhan, B. N., Connolley, T., Basham, M., &amp; Ahmed, S. I.
(2022). U-Net Segmentation Methods for Variable-Contrast XCT Images of
Methane-Bearing Sand Using Small Training Datasets. Earth; Space Science
Open Archive.
https://doi.org/10.1002/essoar.10506807.2</unstructured_citation>
</citation>
<citation key="tun_massively_2021">
<article_title>A massively multi-scale approach to
characterizing tissue architecture by synchrotron micro-CT applied to
the human placenta</article_title>
<author>Tun</author>
<journal_title>Journal of The Royal Society
Interface</journal_title>
<issue>179</issue>
<volume>18</volume>
<doi>10.1098/rsif.2021.0140</doi>
<cYear>2021</cYear>
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Lewis, R. M., Johnstone, E. D., Brownbill, P., Darrow, M., &amp;
Chernyavsky, I. L. (2021). A massively multi-scale approach to
characterizing tissue architecture by synchrotron micro-CT applied to
the human placenta. Journal of The Royal Society Interface, 18(179),
20210140. https://doi.org/10.1098/rsif.2021.0140</unstructured_citation>
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<article_title>Segmentation models pytorch</article_title>
<author>Yakubovskiy</author>
<journal_title>GitHub repository</journal_title>
<cYear>2020</cYear>
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</citation>
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<article_title>SuRVoS 2: Accelerating Annotation and
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<author>Pennington</author>
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<article_title>SuRVoS2</article_title>
<author>Pennington</author>
<journal_title>GitHub repository</journal_title>
<cYear>2018</cYear>
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Luengo, I., &amp; Basham, M. (2018). SuRVoS2. In GitHub repository.
GitHub.
https://github.com/DiamondLightSource/SuRVoS2</unstructured_citation>
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<citation key="perslev_github_2019">
<article_title>Multi-planar U-net</article_title>
<author>Perslev</author>
<journal_title>GitHub repository</journal_title>
<cYear>2019</cYear>
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Multi-planar U-net. In GitHub repository. GitHub.
https://github.com/perslev/MultiPlanarUNet</unstructured_citation>
</citation>
<citation key="tekawade_github_2020">
<article_title>CTSegNet</article_title>
<author>Tekawade</author>
<journal_title>GitHub repository</journal_title>
<cYear>2020</cYear>
<unstructured_citation>Tekawade, A., &amp; Igel, C. (2020).
CTSegNet. In GitHub repository. GitHub.
https://github.com/aniketkt/CTSegNet</unstructured_citation>
</citation>
<citation key="wolny_github_2019">
<article_title>Pytorch-3dunet</article_title>
<author>Wolny</author>
<journal_title>GitHub repository</journal_title>
<cYear>2019</cYear>
<unstructured_citation>Wolny, A. (2019). Pytorch-3dunet. In
GitHub repository. GitHub.
https://github.com/wolny/pytorch-3dunet</unstructured_citation>
</citation>
<citation key="lee_deepem_2018">
<article_title>DeepEM</article_title>
<author>Lee</author>
<journal_title>GitHub repository</journal_title>
<cYear>2018</cYear>
<unstructured_citation>Lee, K., &amp; Turner, N. L. (2018).
DeepEM. In GitHub repository. GitHub.
https://github.com/seung-lab/DeepEM</unstructured_citation>
</citation>
<citation key="lin2021pytorch">
<article_title>PyTorch connectomics: A scalable and flexible
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<author>Lin</author>
<journal_title>arXiv preprint
arXiv:2112.05754</journal_title>
<doi>10.48550/arXiv.2112.05754</doi>
<cYear>2021</cYear>
<unstructured_citation>Lin, Z., Wei, D., Lichtman, J., &amp;
Pfister, H. (2021). PyTorch connectomics: A scalable and flexible
segmentation framework for EM connectomics. arXiv Preprint
arXiv:2112.05754.
https://doi.org/10.48550/arXiv.2112.05754</unstructured_citation>
</citation>
<citation key="urakubo_uni-em_2019">
<article_title>UNI-EM: An Environment for Deep Neural
Network-Based Automated Segmentation of Neuronal Electron Microscopic
Images</article_title>
<author>Urakubo</author>
<journal_title>Scientific Reports</journal_title>
<issue>1</issue>
<volume>9</volume>
<doi>10.1038/s41598-019-55431-0</doi>
<issn>2045-2322</issn>
<cYear>2019</cYear>
<unstructured_citation>Urakubo, H., Bullmann, T., Kubota,
Y., Oba, S., &amp; Ishii, S. (2019). UNI-EM: An Environment for Deep
Neural Network-Based Automated Segmentation of Neuronal Electron
Microscopic Images. Scientific Reports, 9(1), 19413.
https://doi.org/10.1038/s41598-019-55431-0</unstructured_citation>
</citation>
<citation key="lin_pth_connec_github_2019">
<article_title>PyTorch connectomics</article_title>
<author>Lin</author>
<journal_title>GitHub repository</journal_title>
<cYear>2019</cYear>
<unstructured_citation>Lin, Z., Lu, Y., Belhamissi, M.,
Banerjee, A., Lauenburg, L., Swaroop, K. K., Wei, D., &amp; Pfister, H.
(2019). PyTorch connectomics. In GitHub repository. GitHub.
https://github.com/zudi-lin/pytorch_connectomics</unstructured_citation>
</citation>
<citation key="wu_neutorch_2021">
<article_title>Neutorch</article_title>
<author>Wu</author>
<journal_title>GitHub repository</journal_title>
<cYear>2021</cYear>
<unstructured_citation>Wu, J. (2021). Neutorch. In GitHub
repository. GitHub.
https://github.com/flatironinstitute/neutorch</unstructured_citation>
</citation>
<citation key="NEURIPS2019_9015">
<article_title>PyTorch: An imperative style,
high-performance deep learning library</article_title>
<author>Paszke</author>
<journal_title>Advances in neural information processing
systems 32</journal_title>
<cYear>2019</cYear>
<unstructured_citation>Paszke, A., Gross, S., Massa, F.,
Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein,
N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison,
M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S.
(2019). PyTorch: An imperative style, high-performance deep learning
library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dAlché-Buc, E.
Fox, &amp; R. Garnett (Eds.), Advances in neural information processing
systems 32 (pp. 8024–8035). Curran Associates, Inc.
http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf</unstructured_citation>
</citation>
</citation_list>
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