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Automated brain extraction of multi-sequence MRI using artificial neural networks

arXiv 1901.11341 ↗First posted 31 Jan 2019cs.CVcited by 134

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Abstract (arXiv)

Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aims to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.11 points for the DICE coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility our HD-BET prediction algorithm is made freely available (http://www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data.

Key facts. Cited by 134 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Jan 2019.

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Citing papers per year: 2021: 9; 2022: 16; 2023: 19; 2024: 19; 2025: 38; 2026: 33.

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  2. 2024The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRIcited by 69
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  4. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRIcited by 45
  5. 2021The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patientscited by 32
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  7. 2022Shifts 2.0: Extending The Dataset of Real Distributional Shiftscited by 24
  8. 2023Diffusion Models for Contrast Harmonization of Magnetic Resonance Imagescited by 19
  9. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIscited by 18
  10. 2021Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Modelcited by 14
  11. 2022Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021cited by 14
  12. 2023Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Modelcited by 14
38 more
  1. 2025OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detectioncited by 14
  2. 2022Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identificationcited by 13
  3. 2023CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentationcited by 9
  4. 2024Boosting Skull-Stripping Performance for Pediatric Brain Imagescited by 8
  5. 2024A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challengecited by 8
  6. 2022k-strip: A novel segmentation algorithm in k-space for the application of skull strippingcited by 7
  7. 2023Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networkscited by 7
  8. 2023Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentationcited by 7
  9. 2024Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasoundcited by 7
  10. 2024De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacycited by 7
  11. 2024ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Datasetcited by 6
  12. 2021Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysiscited by 5
  13. 2021Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imagingcited by 4
  14. 2023A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translationcited by 4
  15. 2024IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR imagescited by 4
  16. 2024Semi-Supervised Diffusion Model for Brain Age Predictioncited by 4
  17. 2024ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?cited by 4
  18. 2026BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learningcited by 4
  19. 2021Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networkscited by 3
  20. 2022Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patientscited by 3
  21. 2022USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotationscited by 3
  22. 2023Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencodercited by 3
  23. 2023Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Modelcited by 3
  24. 2024IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformationcited by 3
  25. 2025PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generationcited by 3
  26. 2025HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer's Diseasecited by 3
  27. 2025Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Visioncited by 3
  28. 2021Leveraging 3D Information in Unsupervised Brain MRI Segmentationcited by 2
  29. 2022Negligible effect of brain MRI data preprocessing for tumor segmentationcited by 2
  30. 2023SSL^2: Self-Supervised Learning meets Semi-Supervised Learning: Multiple Sclerosis Segmentation in 7T-MRI from large-scale 3T-MRIcited by 2
  31. 2023Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Imagescited by 2
  32. 2024MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRIcited by 2
  33. 2025Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classificationcited by 2
  34. 2025PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planningcited by 2
  35. 2025Diffusion-Driven Generation of Minimally Preprocessed Brain MRIcited by 2
  36. 2026Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Modelscited by 2
  37. 2026SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Trainingcited by 2
  38. 2022Progressive GANomaly: Anomaly detection with progressively growing GANscited by 1

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