Model Overview

An optimized, robust and self-adapting framework for U-Net based medical image segmentation

Model Architecture

The nnU-Net allows the training of two types of networks: 2D U-Net and 3D U-Net to perform semantic segmentation of 3D images, with high accuracy and performance.

The following figure shows the architecture of the 3D U-Net model and its different components. U-Net is composed of a contractive and an expanding path, that aims at building a bottleneck in its centremost part through a combination of convolution, instance norm, and leaky ReLU operations. After this bottleneck, the image is reconstructed through a combination of convolutions and upsampling. Skip connections are added with the goal of helping the backward flow of gradients to improve the training.

Figure 1: The 3D U-Net architecture

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

MSD Task01 - Dataset with complex and heterogeneously-located targets.

Performance

Performance numbers for this model are available in NGC.

References

License

This model was trained using open-source software available in Deep Learning Examples repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.

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\n \n*Figure 1: The 3D U-Net architecture*\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/nnunet_pyt) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/nnUNet).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [MSD Task01](http://medicaldecathlon.com/index.html) - Dataset with complex and heterogeneously-located targets.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/nnunet_pyt/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1809.10486)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/nnunet_pyt)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/nnUNet)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/nnUNet) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","nnUNet PyTorch checkpoint 3D",["D",1670258257517],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/nnunet_3d_pyt_ckpt_eval-fold2","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_107":1220,"_109":1221},"ngcApps",[1173],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1154":1155,"_1156":1157,"_1158":1159,"_1160":1161,"_1162":253,"_139":279,"_1163":1164,"_1165":56,"_1166":86,"_1167":86,"_1168":1169,"_1170":86,"_1171":1172},"amplitude",{"_1151":1152,"_791":-7,"_1153":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1145":1146,"_1147":1148,"_1149":1150},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787834857214,"1.12.0","defaultSearchSuggestions",{"_1031":169,"_1032":1033,"_1034":1035,"_1036":1037},"announcements",[],"consentState",{"_317":1021,"_1022":86,"_1023":31,"_1024":31,"_1025":1026},"consentConfig",{"_299":300,"_301":302,"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318},"showContactAdmin",1787834857215,[],[],[293,294,295,296,297],{"_45":59,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":298,"_55":56},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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