mlsanityData to 14 Oct 2026

SynthStrip: Skull-Stripping for Any Brain Image

arXiv 2203.09974 ↗First posted 18 Mar 2022eess.IV cs.CV physics.med-phcited by 127

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Abstract (arXiv)

The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-based approaches for brain extraction have gained traction in recent years, these methods face a similar burden, as they are only effective for image types seen during the training procedure. To achieve robust skull-stripping across a landscape of imaging protocols, we introduce SynthStrip, a rapid, learning-based brain-extraction tool. By leveraging anatomical segmentations to generate an entirely synthetic training dataset with anatomies, intensity distributions, and artifacts that far exceed the realistic range of medical images, SynthStrip learns to successfully generalize to a variety of real acquired brain images, removing the need for training data with target contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image acquisitions and resolutions across subject populations, ranging from newborn to adult. We show substantial improvements in accuracy over popular skull-stripping baselines -- all with a single trained model. Our method and labeled evaluation data are available at https://w3id.org/synthstrip.

Key facts. Cited by 127 papers in the map (data through 14 Oct 2026). Builds on 13 of its 84 references that are in the map. First posted on arXiv 18 Mar 2022.

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13 of 84 references.

References · 13

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  5. 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,850
  6. 2019Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfacescited by 186
  7. 2018A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocolscited by 49
  8. 2020Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrastcited by 45
  9. 2019Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentationcited by 40
  10. 2020A Learning Strategy for Contrast-agnostic MRI Segmentationcited by 35
  11. 2020Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 yearscited by 11
  12. 2019PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentationcited by 7
1 more
  1. 2021Rapid head-pose detection for automated slice prescription of fetal-brain MRIcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 18; 2024: 29; 2025: 44; 2026: 35.

127 papers.

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  1. 2023UniverSeg: Universal Medical Image Segmentationcited by 91
  2. 2024TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRIcited by 59
  3. 2023A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyondcited by 55
  4. 2024Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledgecited by 35
  5. 2024Learning General-Purpose Biomedical Volume Representations using Randomized Synthesiscited by 29
  6. 2023MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labelingcited by 25
  7. 2023Neuralizer: General Neuroimage Analysis without Re-Trainingcited by 24
  8. 2023Anatomy-aware and acquisition-agnostic joint registration with SynthMorphcited by 19
  9. 2025Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusioncited by 17
  10. 2024Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Modelcited by 16
  11. 2024CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classificationcited by 15
  12. 2023The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)cited by 11
38 more
  1. 2023Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imagingcited by 11
  2. 2024MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registrationcited by 10
  3. 2025Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnosescited by 10
  4. 2025MultiMorph: On-demand Atlas Constructioncited by 9
  5. 2024Boosting Skull-Stripping Performance for Pediatric Brain Imagescited by 8
  6. 2024PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRIcited by 8
  7. 2025A Modality-agnostic Multi-task Foundation Model for Human Brain Imagingcited by 8
  8. 2025Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generationcited by 8
  9. 2023Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networkscited by 7
  10. 2023Fetal-BET: Brain Extraction Tool for Fetal MRIcited by 7
  11. 2024Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRIcited by 7
  12. 2024De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacycited by 7
  13. 2024VMambaMorph: a Multi-Modality Deformable Image Registration Framework based on Visual State Space Model with Cross-Scan Modulecited by 6
  14. 2024UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentationscited by 6
  15. 2024VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysiscited by 6
  16. 2025Domain-randomized deep learning for neuroimage analysiscited by 6
  17. 2025CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesiscited by 5
  18. 2025Unleashing the Power of Image-Tabular Self-Supervised Learning via Breaking Cross-Tabular Barrierscited by 5
  19. 2023Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processingcited by 4
  20. 2023Boundary-weighted logit consistency improves calibration of segmentation networkscited by 4
  21. 2023A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translationcited by 4
  22. 2023APIS: A paired CT-MRI dataset for ischemic stroke segmentation challengecited by 4
  23. 2024Neurovascular Segmentation in sOCT with Deep Learning and Synthetic Training Datacited by 4
  24. 2024ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?cited by 4
  25. 2024Learn2Synth: Learning Optimal Data Synthesis Using Hypergradients for Brain Image Segmentationcited by 4
  26. 2025Learning accurate rigid registration for longitudinal brain MRI from synthetic datacited by 4
  27. 2025Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomizationcited by 4
  28. 2025Constitution or Collapse? Exploring Constitutional AI with Llama 3-8Bcited by 4
  29. 2025MR-CLIP: Efficient Metadata-Guided Learning of MRI Contrast Representationscited by 4
  30. 2025Benchmarking GANs, Diffusion Models, and Flow Matching for T1w-to-T2w MRI Translationcited by 4
  31. 2026Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysiscited by 4
  32. 2024Large Language Models for Multimodal Deformable Image Registrationcited by 3
  33. 2025PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generationcited by 3
  34. 2025How We Won the ISLES'24 Challenge by Preprocessingcited by 3
  35. 2025Medverse: A Universal Model for Full-Resolution 3D Medical Image Segmentation, Transformation and Enhancementcited by 3
  36. 2025Metadata-Aligned 3D MRI Representations for Contrast Understanding and Quality Controlcited by 3
  37. 2025AtlasMorph: Learning conditional deformable templates for brain MRIcited by 3
  38. 2023MELAGE: A purely python based Neuroimaging software (Neonatal)cited by 2

50 most cited of 127 All 127 (JSON)