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

By Fabian Isensee, Paul F. Jaeger, Simon A. A. Kohl, Jens Petersen, Klaus Hermann Maier-Hein. Nature Methods, 2020

9,406 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f28e387d4229c5f690ce4570a391c0f47e7155c7

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Citations in the corpus

Large-scale pancreatic cancer detection via non-contrast CT and deep learning (2023, positive): “The first stage (Stage 1) involves pancreas localization, using an nnU-Net model 23 .”
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation (2023, positive): “We utilized the BTCV dataset (Fang and Yan 2020), a widely-used and publicly available benchmark with twelve anatomies as the benchmark.”
Diffusion models in medical imaging: A comprehensive survey (2022, methodology): “nnU-Net [112] was leveraged to examine the performance.”
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge (2021, methodology): “Four teams used the nnUNet [33] (which includes UNet architectures in 2D and 3D as well as a cascaded UNet) as their baseline segmentation model (P1-P3 & P9).”
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining (2021, neutral): “nnUNet [39]3: A state-of-the-art supervised approach, very similar to the T1 baseline, except that the architecture, augmentation, pre- and postprocessing are automated with respect to the (real) T1 input data.”
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection (2023, background): “These works are often specialized for single organ [59, 96, 29, 38] or single task, i.e., organ segmentation [91, 22, 68, 7] or tumor detection [8, 79, 81].”
U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation (2024, methodology): “For medical image segmentation, U-Mamba [56] and SegMamba [88] have proposed task-specific architectures with Mamba blocks respectively based on nn-UNet [32] and Swin UNETR [22], respectively, achieving promising results in various visual tasks and demonstrating the potential of SSMs in vision.”
Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining (2024, positive): “We implemented Swin-UMamba on top of the well-established nnU-Net framework [11].”
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation (2022, methodology): “Methods for single abdominal organ segmentation The single organ segmentation has been the dominant task for decades, where numerous solutions have been developed [13, 9, 30, 21, 4, 10, 25].”
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model (2022, methodology): “In the table, ResUnet [11] and BEAL [18] are proposed for optic disc/cup segmentation, TransBTS [3] and EnsemDiff [19] are proposed for the brain tumor segmentation, MTSeg [20] and UltraUNet [21] are proposed for the Thyroid Nodule segmentation, CENet [22], MRNet [1], SegNet [23], nnUNet [24] and Tr”
The Medical Segmentation Decathlon (2021, methodology): “The method won 33 out of 53 tasks with a median rank of 1 (interquartile range (IQR) of (1;2)) in the 53 tasks [8], for example being the winning method of the famous BraTS challenge in 2020 9 .”
Diffusion Models for Implicit Image Segmentation Ensembles (2021, methodology): “The automated segmentation of lesions in medical images with machine learning has shown good performances (Isensee et al., 2021) and is ready for clinical application to support diagnosis (Sharrock et al.”
EMCAD: Efficient Multi-Scale Convolutional Attention Decoding for Medical Image Segmentation (2024, methodology): “A variety of U-shaped convolutional neural network (CNN) architectures [20, 24, 37, 41, 44, 62], notably UNet [44], UNet++ [62], UNet3+ [24], and nnU-Net [19], have become standard techniques for this purpose, achieving high-quality, high-resolution segmentation output.”
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation (2024, methodology): “This is evident as winning solutions in medical image segmentation competitions still predominantly rely on CNNs 6 , such as nnU-Net [22] and SegResNet [39].”
TransMorph: Transformer for unsupervised medical image registration (2021, neutral): “…challenge of 2015 (Ronneberger et al., 2015), ConvNet-based methods have become a major focus of attention in medical image analysis fields, such as tumor segmentation (Isensee et al., 2021; Zhou et al., 2019), image reconstruction (Zhu et al., 2018), and disease diagnostics (Lian et al., 2018).”