ArticleImaging neuroscience (Cambridge, Mass.)
Monitoring morphometric drift in lifelong learning segmentation of the spinal cord.
Article in Imaging neuroscience (Cambridge, Mass.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spinal cord imaging in multiple sclerosis: A vital component we can no longer overlook.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Pooled it
- Charting Cervical Spinal Cord Morphometry Across the Lifespan.Human brain mapping · 2026Article
- Charting Cervical Spinal Cord Morphometry Across the Lifespan.bioRxiv : the preprint server for biology · 2026Article
- Segmentation of spinal rootlets across MRI contrasts with RootletSeg.Scientific reports · 2026Article
- Spatial distribution of spinal cord fMRI activity with electrocutaneous stimulation.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Building a library of acute traumatic spinal cord injury images across Canada: a retrospective cohort study protocol.BMJ open · 2025Article
- Fully Automated Segmentation of Cervical Spinal Cord in Sagittal MR Images Using Swin-Unet Architectures.Journal of clinical medicine · 2025Article
- Rootlets-based registration to the PAM50 spinal cord template.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Navigator readouts with optimized processing improve spinal cord and gray matter segmentability in multi-echo gradient-echo imaging.Imaging neuroscience (Cambridge, Mass.)Article
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55 authors.
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Abstract
Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and injuries affecting the spinal cord. For instance, the spinal cord cross-sectional area can be used to monitor cord atrophy in multiple sclerosis and to characterize compression in degenerative cervical myelopathy. While robust, automatic segmentation methods to a wide variety of contrasts and pathologies have been developed over the past few years, whether their predictions are stable as the model is updated using new datasets has not been assessed. This is particularly important for deriving normative values from healthy participants. In this study, we present a spinal cord segmentation model trained on a multisite (
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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.