ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024
BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- MindGrab: A spectrally-motivated architecture for accessible deep learning in neuroimaging.NeuroImage · 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
- Feasibility of Non-Sedate Magnetic Resonance Imaging for Children with Cerebral Palsy: Tolerance and Structural Analysis Considerations.Children (Basel, Switzerland) · 2026Article
- Repeatable tract-based diffusion metrics on a portable 64 mT MRI: a foundation for global population neuroscience.Frontiers in neuroimaging · 2026Article
- Deep infant brain segmentation from multi-contrast MRI.Conference record. Asilomar Conference on Signals, Systems & Computers · 2025Article
- The Impacts of Enlarged Subarachnoid Space on Brain Growth and Cortex Maturation in Very Preterm Infants.Diagnostics (Basel, Switzerland) · 2025Article
- PediMS: A Pediatric Multiple Sclerosis Lesion Segmentation Dataset.Scientific data · 2025Article
- Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and Maximizing Benefits.IEEE signal processing magazine · 2025Article
- Joint spatial associations of amyloid beta and tau pathology in Down syndrome and preclinical Alzheimer's disease: Cross-sectional associations with early cognitive impairments.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- MultiMorph: On-demand Atlas Construction.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- Skull stripping tools in pediatric T2-weighted MRI scans: a retrospective evaluation of segmentation performance.Frontiers in neuroscience · 2025Article
- Synthetic data in generalizable, learning-based neuroimaging.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Anatomy-aware and acquisition-agnostic joint registration with SynthMorph.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Sex differences in brain MRI using deep learning toward fairer healthcare outcomes.Frontiers in computational neuroscience · 2024Article
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6 authors.
Funding
Abstract
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.
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