Automated Design of Deep Learning Methods for Biomedical Image Segmentation

2019/04/30 by Fabian Isensee, Paul F. Jäger, Paul F. Jaeger +3 · 554 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Advanced Neural Network Applications #Cell Image Analysis Techniques #Medical Imaging and Analysis #cs.CV

paper · pdf · doi:10.1038/s41592-020-01008-z

published as Nature Methods (2020) · * Fabian Isensee and Paul F. Jäger share the first authorship

arxiv created 2020/04/02 · openalex created_date 2020/04/10 · crossref issued 2020/12/07 · crossref published 2020/12/07 · crossref published-online 2020/12/07 · openalex publication_date 2020/12/07 · crossref created 2020/12/07 · arxiv updated 2020/12/09 · crossref published-print 2021/02/01 · crossref deposited 2023/05/20 · crossref indexed 2026/07/30 · openalex updated_date 2026/07/31

Abstract

Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithms enable 3D image analysis and quantification in many applications, the design of respective specialised solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We propose nnU-Net, a deep learning framework that condenses the current domain knowledge and autonomously takes the key decisions required to transfer a basic architecture to different datasets and segmentation tasks. Without manual tuning, nnU-Net surpasses most specialised deep learning pipelines in 19 public international competitions and sets a new state of the art in the majority of the 49 tasks. The results demonstrate a vast hidden potential in the systematic adaptation of deep learning methods to different datasets. We make nnU-Net publicly available as an open-source tool that can effectively be used out-of-the-box, rendering state of the art segmentation accessible to non-experts and catalyzing scientific progress as a framework for automated method design.

Citations

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
• Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms
• PyTorch: An Imperative Style, High-Performance Deep Learning Library

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• Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation
• RAPS-3D: Efficient interactive segmentation for 3D radiological imaging
• SAM+D: Parameter-Efficient Dimensional Lifting of SAM-Family Models via Depth-Routed LoRA and Depth Shifting
• Omni-Fusion of Spatial and Spectral for Hyperspectral Image Segmentation
• Airway Segmentation Network for Enhanced Tubular Feature Extraction
• First Investigation of Deep Learning for Intraoperative Gauze Segmentation in Minimally Invasive Abdominal Surgery
• DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation
• Beyond Manual Annotation: A Human-AI Collaborative Framework for Medical Image Segmentation Using Only "Better or Worse" Expert Feedback
• From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
• PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning
• AnatomyCarve: A VR occlusion management technique for medical images based on segment-aware clipping
• Learning Segmentation from Radiology Reports
• Biomechanics of contagious yawning: Insights into cranio-cervical fluid dynamics and kinematic consistency
• SPIDER: Structure-Preferential Implicit Deep Network for Biplanar X-ray Reconstruction
• UltraSAM3: A Concept-Driven Foundation Model for Universal Ultrasound Image Segmentation
• Scanner-based real-time automated volumetry reporting of the fetus, amniotic fluid, placenta and umbilical cord for fetal MRI at 0.55T
• PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation
• PLUS: Plug-and-Play Enhanced Liver Lesion Diagnosis Model on Non-Contrast CT Scans
• Unsupervised Deep Learning for Blood-Brain Barrier Leakage Detection in Diffuse Glioma Using Dynamic Contrast-enhanced MRI
• SAMed-2: Selective Memory Enhanced Medical Segment Anything Model
• A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities
• Causal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation
• Prompt learning with bounding box constraints for medical image segmentation
• TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation
• CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR
• Calibrated Self-supervised Vision Transformers Improve Intracranial Arterial Calcification Segmentation from Clinical CT Head Scans
• Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using Hölder Divergence and Mutual Information-Enhanced Knowledge Transfer
• Autoadaptive Medical Segment Anything Model
• PanTS: The Pancreatic Tumor Segmentation Dataset
• DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images
• MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound
• Similarity Memory Prior is All You Need for Medical Image Segmentation
• Fully automatic anatomical landmark localization and trajectory planning for navigated external ventricular drain placement
• MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis
• ShapeKit
• Towards Markerless Intraoperative Tracking of Deformable Spine Tissue
• Image segmentation of treated and untreated tumor spheroids by fully convolutional networks
• Using a fully automated, quantitative fissure integrity score extracted from chest CT scans of emphysema patients to predict endobronchial valve response
• Single Image Test-Time Adaptation via Multi-View Co-Training
• Deep Learning-Based Semantic Segmentation for Real-Time Kidney Imaging and Measurements with Augmented Reality-Assisted Ultrasound
• GUSL: A Novel and Efficient Machine Learning Model for Prostate Segmentation on MRI
• BPD-Neo: An MRI Dataset for Lung-Trachea Segmentation with Clinical Data for Neonatal Bronchopulmonary Dysplasia
• Frequency-enhanced Multi-granularity Context Network for Efficient Vertebrae Segmentation
• CA-Diff: Collaborative Anatomy Diffusion for Brain Tissue Segmentation
• Cardiovascular disease classification using radiomics and geometric features from cardiac CT
• Advanced Deep Learning Techniques for Automated Segmentation of Type B Aortic Dissections
• HyperSORT: Self-Organising Robust Training with hyper-networks
• A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation
• Robust Deep Learning for Myocardial Scar Segmentation in Cardiac MRI with Noisy Labels
• Text-guided multi-stage cross-perception network for medical image segmentation
• GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
• AI-Driven MRI-based Brain Tumour Segmentation Benchmarking
• NeRF-based CBCT Reconstruction needs Normalization and Initialization
• SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting
• General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound
• MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation
• Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention
• Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster
• FIB-SEM as a Volume Electron Microscopy Approach to Study Cellular Architectures in SARS-CoV-2 and Other Viral Infections: A Practical Primer for a Virologist
• OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model
• TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration
• Spatially-Aware Evaluation of Segmentation Uncertainty
• VesselSDF: Distance Field Priors for Vascular Network Reconstruction
• Pediatric Pancreas Segmentation from MRI Scans with Deep Learning
• SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI
• Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention
• CLAIM: Clinically-Guided LGE Augmentation for Realistic and Diverse Myocardial Scar Synthesis and Segmentation
• Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing
• Conquering the Retina: Bringing Visual in-Context Learning to OCT
• A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
• Assessment of Optimizers and their Performance in Autosegmenting Lung Tumors
• Latent Anomaly Detection: Masked VQ-GAN for Unsupervised Segmentation in Medical CBCT
• Improving Prostate Gland Segmentation Using Transformer based Architectures
• Automatic Multi-View X-Ray/CT Registration Using Bone Substructure Contours
• TVFNet: text and visual attention feature fusion network for multi-lesion segmentation of diabetic retinopathy
• Unleashing Diffusion and State Space Models for Medical Image Segmentation
• Shape-aware Sampling Matters in the Modeling of Multi-Class Tubular Structures
• 3D Skin Segmentation Methods in Medical Imaging: A Comparison
• Predicting Patient Survival with Airway Biomarkers using nn-Unet/Radiomics
• Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
• crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
• BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
• Deep learning-driven automated high-content dSTORM imaging with a scalable open-source toolkit
• Training and assessing convolutional neural network performance in automatic vascular segmentation using Ga-68 DOTATATE PET/CT
• Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation
• Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective
• CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain
• FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks
• LinGuinE: Longitudinal Guidance Estimation for Volumetric Tumour Segmentation
• ADNP-15: An Open-Source Histopathological Dataset for Neuritic Plaque Segmentation in Human Brain Whole Slide Images with Frequency Domain Image Enhancement for Stain Normalization
• Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
• Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework
• Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning
• Beyond Pixel Agreement: Large Language Models as Clinical Guardrails for Reliable Medical Image Segmentation
• Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?
• Reinforced Correlation Between Vision and Language for Precise Medical AI Assistant
• A versatile foundation model for cine cardiac magnetic resonance image analysis tasks
• Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding
• Revolutionizing Brain Tumor Imaging: Generating Synthetic 3D FA Maps from T1-Weighted MRI using CycleGAN Models
• Segmenting France Across Four Centuries
• Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI
• TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
• Enjoying Information Dividend: Gaze Track-based Medical Weakly Supervised Segmentation
• MAIA: A Collaborative Medical AI Platform for Integrated Healthcare Innovation
• ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions
• Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation
• Towards Scalable Language-Image Pre-training for 3D Medical Imaging
• Good Enough: Is it Worth Improving your Label Quality?
• Learning to Upscale 3D Segmentations in Neuroimaging
• IntelliCardiac: An Intelligent Platform for Cardiac Image Segmentation and Classification
• Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants
• Zig-RiR: Zigzag RWKV-in-RWKV for Efficient Medical Image Segmentation
• CARE: Confidence-aware Ratio Estimation for Medical Biomarkers
• LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation
• DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction
• Spatially-Adaptive Gradient Re-parameterization for 3D Large Kernel Optimization
• Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering
• SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours
• MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images
• TK-Mamba: Marrying KAN With Mamba for Text-Driven 3D Medical Image Segmentation
• Explainable Anatomy-Guided AI for Prostate MRI: Foundation Models and In Silico Clinical Trials for Virtual Biopsy-based Risk Assessment
• Pixels to Prognosis: Harmonized Multi-Region CT-Radiomics and Foundation-Model Signatures Across Multicentre NSCLC Data
• Semi-Supervised Medical Image Segmentation via Dual Networks
• FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation
• Anatomy-Guided Multitask Learning for MRI-Based Classification of Placenta Accreta Spectrum and its Subtypes
• How We Won the ISLES'24 Challenge by Preprocessing
• Render-FM: A Foundation Model for Real-time Photorealistic Volumetric Rendering
• CMRINet: Joint Groupwise Registration and Segmentation for Cardiac Function Quantification from Cine-MRI
• Auto-nnU-Net: Towards Automated Medical Image Segmentation
• TAGS: 3D Tumor-Adaptive Guidance for SAM
• Reconsider the Template Mesh in Deep Learning-based Mesh Reconstruction
• UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation
• CT-PrepAgent: Bounded Policy and Controlled Execution for Adaptive CT Data Preparation
• Bronchovascular Tree-Guided Weakly Supervised Learning Method for Pulmonary Segment Segmentation
• Paradigm Shift in Infrastructure Inspection Technology: Leveraging High-performance Imaging and Advanced AI Analytics to Inspect Road Infrastructure
• Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification
• MedVKAN: Efficient Feature Extraction with Mamba and KAN for Medical Image Segmentation
• Multimodal domain adaptation under label shift and blockwise missing modalities
• Patient-Specific Autoregressive Models for Organ Motion Prediction in Radiotherapy
• Accuracy of an nnUNet Neural Network for the Automatic Segmentation of Intracranial Aneurysms, Their Parent Vessels, and Major Cerebral Arteries from MRI-TOF
• NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans
• Generalizable cardiac substructures segmentation from contrast and non-contrast CTs using pretrained transformers
• GOUHFI: a novel contrast- and resolution-agnostic segmentation tool for Ultra-High Field MRI
• Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance
• RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI
• Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided
• ROIsGAN: A Region Guided Generative Adversarial Framework for Murine Hippocampal Subregion Segmentation
• Data-Agnostic Augmentations for Unknown Variations: Out-of-Distribution Generalisation in MRI Segmentation
• Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency
• Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platform
• Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentation
• MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation
• Recent Advances in Medical Imaging Segmentation: A Survey
• Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
• A Large-scale Benchmark on Geological Fault Delineation Models: Domain Shift, Training Dynamics, Generalizability, Evaluation and Inferential Behavior
• Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions
• Probabilistic approach to longitudinal response prediction: application to radiomics from brain cancer imaging
• Robust Kidney Abnormality Segmentation: A Validation Study of an AI-Based Framework
• MAIS: Memory-Attention for Interactive Segmentation
• Skull stripping with purely synthetic data
• Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment
• BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation
• Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort
• ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesis
• How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
• Regression is all you need for medical image translation
• Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2
• Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement
• Monitoring morphometric drift in lifelong learning segmentation of the spinal cord
• Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging
• The Evolution of Artificial Intelligence in Nuclear Medicine
• Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma
• SPROUT: A User-friendly, Scalable Toolkit for Multi-class Segmentation of Volumetric Images
• A Unified 2D Framework for DeepLesion Detection, Segmentation and Short Report Generation
• Automated segmentation of pediatric neuroblastoma on multi-modal MRI: Results of the SPPIN challenge at MICCAI 2023
• 7 Tesla Quantitative MRI and Machine Learning for Exploratory Motor Subtype Stratification and Diagnosis in Parkinson's Disease
• UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation
• Anomaly-Driven Approach for Enhanced Prostate Cancer Segmentation
• Automated Optical Density Normalization for Myelin Quantification: Cross-Modal Validation with 7T Ex Vivo MRI
• FSS-Net: frequency-spatial synergy network with wavelet attention for carotid artery ultrasound segmentation
• LymphAtlas- A Unified Multimodal Lymphoma Imaging Repository Delivering AI-Enhanced Diagnostic Insight
• RadSAM: Segmenting 3D radiological images with a 2D promptable model
• Multi-Catheter Digitization in Brachytherapy via Few-Shot Synthetic-to-Real Learning and Structure-Aware Tracking
• VIDS: A Verified Imaging Dataset Standard for Medical AI
• Imaging Biomarkers for Neurodegenerative Diseases from Detailed Segmentation of Medial Temporal Lobe Subregions on in vivo Brain MRI Using Upsampling Strategy Guided by High-resolution ex vivo MRI
• Federated Client-tailored Adapter for Medical Image Segmentation
• Towards a deep learning approach for classifying treatment response in glioblastomas
• VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing
• 3D Deep-learning-based Segmentation of Human Skin Sweat Glands and Their 3D Morphological Response to Temperature Variations
• Volumetric medical image segmentation via fully 3D adaptation of Segment Anything Model
• ProGiDiff: Prompt-Guided Diffusion-Based Medical Image Segmentation
• CRIL-U-Net: Compact Ratio-Interaction Learning for Focal Cortical Dysplasia Segmentation from T1w and FLAIR MRI
• NeuroMosaic: Anatomically Grounded Multimodal Large Language Modeling for Molecularly Aware Glioma Reasoning from 3D MRI and Clinical Narratives
• When Oracle Conditioning Misleads Deployment: Conditioning-Availability Bias in Echocardiographic Segmentation
• Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation
• Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation
• Benchmarking the Reproducibility of Brain MRI Segmentation Across Scanners and Time
• DINs: Deep Interactive Networks for Neurofibroma Segmentation in Neurofibromatosis Type 1 on Whole-Body MRI
• MedM-VL: What Makes a Good Medical LVLM?
• CALF: A Conditionally Adaptive Loss Function to Mitigate Class-Imbalanced Segmentation
• ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
• Med-2D SegNet: A Light Weight Deep Neural Network for Medical 2D Image Segmentation
• ViG3D-UNet: Volumetric Vascular Connectivity-Aware Segmentation via 3D Vision Graph Representation
• A Novel Convolutional Neural Network for Automated Multiple Sclerosis Brain Lesion Segmentation
• Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
• Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond
• Hierarchical Feature Learning for Medical Point Clouds via State Space Model
• Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation
• Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation
• Quantum Error Mitigation with Diffusion-Like Models
• Text-Guided Refinement of Multi-sequence Glioma Subregion Segmentation with a Vision-Language Foundation Model
• From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised Medical Image Segmentation
• MediSee: Reasoning-based Pixel-level Perception in Medical Images
• PraNet-V2: Dual-Supervised Reverse Attention for Medical Image Segmentation
• Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)
• Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg
• HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss
• Conditional Conformal Risk Adaptation
• Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation
• Longitudinal Assessment of Lung Lesion Burden in CT
• Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT
• nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection
• CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images
• HRMedSeg: Unlocking High-resolution Medical Image segmentation via Memory-efficient Attention Modeling
• SlicerNNInteractive: A 3D Slicer extension for nnInteractive
• Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors
• Biomechanical Constraints Assimilation in Deep-Learning Image Registration: Application to sliding and locally rigid deformations
• MedSAM2: Segment Anything in 3D Medical Images and Videos
• Multi-encoder nnU-Net outperforms transformer models with self-supervised pretraining
• ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans
• Two-Stage nnU-Net for Automatic Multi-class Bi-Atrial Segmentation from LGE-MRIs