{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nnu-net-self-adapting-framework-for-u-net","title":"nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation","arxiv_id":"1809.10486","date":"2018-09-27","proceeding":null,"authors":["Fabian Isensee","Jens Petersen","Andre Klein","David Zimmerer","Paul F. Jaeger","Simon Kohl","Jakob Wasserthal","Gregor Koehler","Tobias Norajitra","Sebastian Wirkert","Klaus H. Maier-Hein"],"abstract":"The U-Net was presented in 2015. With its straight-forward and successful\narchitecture it quickly evolved to a commonly used benchmark in medical image\nsegmentation. The adaptation of the U-Net to novel problems, however, comprises\nseveral degrees of freedom regarding the exact architecture, preprocessing,\ntraining and inference. These choices are not independent of each other and\nsubstantially impact the overall performance. The present paper introduces the\nnnU-Net ('no-new-Net'), which refers to a robust and self-adapting framework on\nthe basis of 2D and 3D vanilla U-Nets. We argue the strong case for taking away\nsuperfluous bells and whistles of many proposed network designs and instead\nfocus on the remaining aspects that make out the performance and\ngeneralizability of a method. We evaluate the nnU-Net in the context of the\nMedical Segmentation Decathlon challenge, which measures segmentation\nperformance in ten disciplines comprising distinct entities, image modalities,\nimage geometries and dataset sizes, with no manual adjustments between datasets\nallowed. At the time of manuscript submission, nnU-Net achieves the highest\nmean dice scores across all classes and seven phase 1 tasks (except class 1 in\nBrainTumour) in the online leaderboard of the challenge.","url_abs":"http://arxiv.org/abs/1809.10486v1","url_pdf":"http://arxiv.org/pdf/1809.10486v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nnu-net-self-adapting-framework-for-u-net","repo_url":"https://github.com/MIC-DKFZ/nnunet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"nnu-net-self-adapting-framework-for-u-net","repo_url":"https://github.com/gift-surg/MONAIfbs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"nnu-net-self-adapting-framework-for-u-net","repo_url":"https://github.com/justld/nnunet_paddle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"paddle","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"nnu-net-self-adapting-framework-for-u-net","repo_url":"https://github.com/MS-Mind/MS-Code-06/tree/main/nnUNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"nnu-net-self-adapting-framework-for-u-net","repo_url":"https://github.com/MS-Mind/MS-Code-08/tree/main/nnUNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"nnu-net-self-adapting-framework-for-u-net","repo_url":"https://github.com/MindSpore-paper-code-2/code2/tree/main/nnUNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"nnu-net-self-adapting-framework-for-u-net","repo_url":"https://github.com/NVIDIA/DeepLearningExamples/tree/ddbcd54056e8d1bc1c4d5a8ab34cb570ebea1947/PyTorch/Segmentation/nnUNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"nnu-net-self-adapting-framework-for-u-net","repo_url":"https://github.com/PaddlePaddle/PaddleSeg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null},{"paper_slug":"nnu-net-self-adapting-framework-for-u-net","repo_url":"https://github.com/code-implementation1/Code6/tree/main/nnUNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/medical-image-segmentation-on-medical","task":"Medical Image Segmentation","dataset":"Medical Segmentation Decathlon","model":"nnUNet","rank_in_archive_order":3,"of":5,"metrics":{"Dice (Average)":"77.89","NSD":"88.09"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-synapse-multi","task":"Medical Image Segmentation","dataset":"Synapse multi-organ CT","model":"nnUNet","rank_in_archive_order":4,"of":23,"metrics":{"Avg DSC":"88.80","Avg HD":"10.78"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.10486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.10486"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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