article Open AccessTop 1% cited
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Nature Methods · 2020 · Vol. 18(2) · pp. 203–211
Fabian Isensee✉(German Cancer Research Center)Paul F. Jaeger(German Cancer Research Center)Simon A. A. Kohl(German Cancer Research Center)Jens Petersen(German Cancer Research Center)Klaus H. Maier-Hein(German Cancer Research Center)
Advanced Neural Network ApplicationsCell Image Analysis TechniquesMedical Imaging and AnalysisSegmentationRendering (computer graphics)Image segmentationKey (lock)Deep learningField (mathematics)Segmentation-based object categorizationScale-space segmentation
MeSH terms
Deep LearningAlgorithmsImage Processing, Computer-AssistedNeural Networks, Computer
Citations
7,759
FWCI
221.69
field-weighted impact
References
50
Percentile
100%
vs. same field & year
Citations per year
Cited by
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
IEEE Transactions on Medical Imaging · 2020 · 1,199 citations
Segment anything in medical images
Nature Communications · 2024 · 2,082 citations
References
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
IEEE Transactions on Medical Imaging · 2014 · 6,268 citations
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Nature Communications · 2014 · 5,021 citations
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
IEEE Transactions on Pattern Analysis and Machine Intelligence · 2017 · 21,645 citations
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
IEEE Transactions on Medical Imaging · 2018 · 2,134 citations
Clinically applicable deep learning for diagnosis and referral in retinal disease
Nature Medicine · 2018 · 2,512 citations
U-Net: deep learning for cell counting, detection, and morphometry
Nature Methods · 2018 · 1,967 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.