Fully Automated and Standardized Segmentation of Adipose Tissue Compartments 
via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies

Küstner, Thomas; Hepp, Tobias; Fischer, Marc; Schwartz, Martin; Fritsche, Andreas; Häring, Hans-Ulrich; Nikolaou, Konstantin; Bamberg, Fabian; Yang, Bin; Schick, Fritz; Gatidis, Sergios; Machann, Jürgen

doi:10.1148/ryai.2020200010

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Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies

Küstner, T., Hepp, T., Fischer, M., Schwartz, M., Fritsche, A., Häring, H.-U., et al. (2020). Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies. Radiology: artificial intelligence, 2(6): e200010. doi:10.1148/ryai.2020200010.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000F-DF5D-F Version Permalink: https://hdl.handle.net/21.11116/0000-000F-DF5E-E

Genre: Journal Article

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https://doi.org/10.1148/ryai.2020200010 (Publisher version) Miscellaneous type of Open Access

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Miscellaneous

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https://doi.org/10.48550/arXiv.2008.02251 (Preprint) Open Access Green

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Creators:
Küstner, Thomas¹, Author
Hepp, Tobias^{1, 2}, Author
Fischer, Marc¹, Author
Schwartz, Martin¹, Author
Fritsche, Andreas¹, Author
Häring, Hans-Ulrich¹, Author
Nikolaou, Konstantin¹, Author
Bamberg, Fabian¹, Author
Yang, Bin¹, Author
Schick, Fritz¹, Author
Gatidis, Sergios¹, Author
Machann, Jürgen ¹, Author

Affiliations:
1External Organizations, ou_persistent22
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647

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Free keywords: Abt. Schölkopf

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Language(s): eng - English

Dates: Published Online: 2020-10-28Date issued: 2020-11

Publication Status: Issued

Pages: 13

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Table of Contents: -

Rev. Type: Peer

Identifiers: BibTex Citekey: Kuestneretal20
DOI: 10.1148/ryai.2020200010
arXiv: 2008.02251

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Title: Radiology: artificial intelligence

Abbreviation : Radiology: Art. Int.

Source Genre: Journal

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Publ. Info: OakBrook, IL : RSNA

Pages: - Volume / Issue: 2 (6) Sequence Number: e200010 Start / End Page: - Identifier: ISSN: 2638-6100
CoNE: https://pure.mpg.de/cone/journals/resource/2638-6100