ArticleNeuroImage2022
SynthStrip: skull-stripping for any brain image.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 261 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
261 citing papers in PubMed, 1 citations in OpenAlex.
- Trial
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- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Post mortem MRI of cholinergic white matter pathways across neurodegenerative diseases.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Weakening of Subcortical and Strengthening of Cortical Visual Pathways Across Early Adolescence.Human brain mapping · 2026Article
- MindGrab: A spectrally-motivated architecture for accessible deep learning in neuroimaging.NeuroImage · 2026Article
- Individual-level metabolic connectivity captures cortical morphology and their coupling strengthens in the ageing brain.European journal of nuclear medicine and molecular imaging · 2026Article
- SynthPET: A 3D generative AI approach for FDG-PET image synthesis from T1-weighted MRI and ASL CBF in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Gray Matter Microstructure Measured Using Diffusion Imaging as a Biomarker of Severity in Lewy Body Diseases.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Anatomical white matter tracts span the cortical hierarchy to support cognitive diversity.Nature human behaviour · 2026Article
- Development and Internal Validation of the CLIMPOUT Score: A Multimodal Prediction Model for Neurological Outcome After Pediatric Intracerebral Hemorrhage.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography.Scientific reports · 2026Article
- A cortical gradient in area 55b links language and somato-cognitive action networks.Research square · 2026Article
- Characterising the Diffusion Functional Signature of Negative BOLD With Interleaved TMS-fMRI in the Human Brain.Human brain mapping · 2026Article
- Morphometric Explanations for Deep Learning Based Neuroimaging Classifiers.Human brain mapping · 2026Article
- Automatic Segmentation of Ischaemic Stroke Lesions Using Transformers and Convolutional Neural Networks Applied to Multimodal Neuroimaging.Sensors (Basel, Switzerland) · 2026Article
- The Associations of cerebral blood flow and white matter hyperintensities with tau and amyloid-beta across the Alzheimer's disease spectrum.Brain imaging and behavior · 2026Article
- Neonatal brain-age models in full- and preterm infants.Developmental cognitive neuroscience · 2026Article
- Deep Learning-Based Automated Detection and Burden Assessment of Paramagnetic Rim Lesions on Quantitative Susceptibility Mapping in Patients With Multiple Sclerosis.Korean journal of radiology · 2026Article
201 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.