ArticleMedical image analysis2023
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 363 papers, 1 of them a synthesis that pooled it.
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Who cites it
363 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Developmental Brain Age Estimation From MRI Data: A Systematic Review of Deep Learning Approaches and Open Datasets.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
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- Advancing the Volumetric Analysis of Ultra-Low-Field Brain MRI Using Image-to-Image Translation.Magnetic resonance in medicine · 2026Article
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- Hippocampal Radiomic Signatures in Multiple Sclerosis Subtypes: A Machine Learning-Based MRI Study.Journal of imaging informatics in medicine · 2026Article
- Improving racial fairness in brain age models using style-transfer synthesis.bioRxiv : the preprint server for biology · 2026Article
- PREDICT-GBM: A multicenter platform advancing personalized glioblastoma radiotherapy planning.NPJ digital medicine · 2026Article
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- Identifing two distinct cortical progression subtypes of Parkinson's disease through multimodal neuroimaging.European journal of nuclear medicine and molecular imaging · 2026Article
- MindGrab: A spectrally-motivated architecture for accessible deep learning in neuroimaging.NeuroImage · 2026Article
- Association Between Anterior Hippocampal Gyrification and Episodic Memory Performance in Neurotypical Young Adults.Hippocampus · 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
- Anatomical white matter tracts span the cortical hierarchy to support cognitive diversity.Nature human behaviour · 2026Article
- Pathology-validated structural and physiologic habitat imaging for differentiating radiation necrosis from tumor recurrence in brain metastases.Journal of neuro-oncology · 2026Article
- In-silico solute transport via perivascular networks in the human intracranial space.Nature communications · 2026Article
- Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography.Scientific reports · 2026Article
- Language-enhanced generative modeling for amyloid PET synthesis from MRI and blood biomarkers.iScience · 2026Article
303 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
Abstract
Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MRI modality, performance can decrease across datasets. Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation strategy where we fully randomise the contrast and resolution of the synthetic training data. Consequently, SynthSeg can segment real scans from a wide range of target domains without retraining or fine-tuning, which enables straightforward analysis of huge amounts of heterogeneous clinical data. Because SynthSeg only requires segmentations to be trained (no images), it can learn from labels obtained by automated methods on diverse populations (e.g., ageing and diseased), thus achieving robustness to a wide range of morphological variability. We demonstrate SynthSeg on 5,000 scans of six modalities (including CT) and ten resolutions, where it exhibits unparallelled generalisation compared with supervised CNNs, state-of-the-art domain adaptation, and Bayesian segmentation. Finally, we demonstrate the generalisability of SynthSeg by applying it to cardiac MRI and CT scans.
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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.