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nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Fabian Isensee; Paul F. Jaeger; Simon A. A. Kohl; Jens Petersen; Klaus H. Maier-Hein · Nature Methods · 2020

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Author / responsible party
Fabian Isensee; Paul F. Jaeger; Simon A. A. Kohl; Jens Petersen; Klaus H. Maier-Hein
Publisher
Nature Methods
Year
2020
Language
English

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APA 7

Isensee, F, Jaeger, P. F, Kohl, S. A. A, Petersen, J, & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. https://doi.org/10.1038/s41592-020-01008-z

MLA

Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." 2020. https://doi.org/10.1038/s41592-020-01008-z.

Chicago

Isensee, Fabian, Paul F. Jaeger, Simon A. A. Kohl, Jens Petersen, and Klaus H. Maier-Hein. 2020. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.". https://doi.org/10.1038/s41592-020-01008-z.

Harvard

Isensee, F. et al. 2020, nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation, Nature Methods, available at: https://doi.org/10.1038/s41592-020-01008-z [Accessed 7 Aug. 2026].

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Title
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Author / contributors
Fabian Isensee; Paul F. Jaeger; Simon A. A. Kohl; Jens Petersen; Klaus H. Maier-Hein
Publisher
Nature Methods
Publication year
2020
Language
English

Subjects

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Computer science; Segmentation; Artificial intelligence; Rendering (computer graphics); Image segmentation; Key (lock); Deep learning; Field (mathematics); Segmentation-based object categorization; Scale-space segmentation; Component (thermodynamics); Set (abstract data type)
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