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pretrained models for 3D semantic image segmentation with nnU-Net

appsOther research productkeyboard_double_arrow_right Other ORP type 31 Mar 2020Publisher:Zenodo
Authors: Isensee, Fabian; Jäger, Paul F.; Kohl, Simon A. A.; Petersen, Jens; Maier-Hein, Klaus H.;

doi: 10.5281/zenodo.3734294 , 10.5281/zenodo.3734293 , 10.5281/zenodo.4485926 , 10.5281/zenodo.4003545

pretrained models for 3D semantic image segmentation with nnU-Net

Abstract

nnU-Net is an out-of-the-box segmentation tool for 3D biomedical image data. It automatically configures U-Net based segmentation pipelines that achieve state of the art result across a broad range of datasets. We have evaluated nnU-Net on 10 biomedical segmentation challenges spanning 19 diverse datasets. This repository contains the trained network parameters for these datasets. Instructions for how to use them are provided at: https://github.com/MIC-DKFZ/nnUNet

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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