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SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans

Radiology Artificial Intelligence(2024)10.1148/ryai.240005Source: DataRank Database

SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans is a research paper published in Radiology Artificial Intelligence (2024). On theSindex it has a DataRank of 0.430. It has been cited 13 times, with 5 citing works in its 1-hop citation network.

N/A

0.430DataRank · unranked

Scored on demand from live citation data

Open Access13 citations · 5 citing works in its network

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (22 grants)Impact (FWCI 1.62)OA: hybridTopics (60)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 92%Citation Network 8%

Base Score Contribution

0.396

From this paper's citation signal

Citation Network Contribution

0.0343

From 3 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 5 citers.

Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends
Journal of the Korean Society of Radiology20252 citations77 referencesContributes 0.014
Fully Automated Segmentation of Cervical Spinal Cord in Sagittal MR Images Using Swin-Unet Architectures
Journal of Clinical Medicine20251 citations49 referencesContributes 0.014
The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review
Spinal Cord20251 citations58 referencesContributes 0.012
Detection of Abnormalities in Spinal Cord Injury MRI Using Image Processing and Machine Learning
Engineering Technology & Applied Science Research20260 citations27 referencesContributes 0.000
Imaging Biomarkers for Compressed Spinal Cord
Neurosurgery Clinics of North America20260 citations50 referencesContributes 0.000

Authors (9)

Jan Valošek ORCID,Andrew C. Smith ORCID,Dario Pfyffer ORCID,Simon Schading‐Sassenhausen ORCID,Lynn Farner ORCID

Live enrichment

PubMed:39503603 PMC:PMC11791505 OpenAlex:https://openalex.org/W4404107931

Canada Research Chair

Grant: CRC-2020-00179

Canadian Institutes of Health Research

Grant: PJT-190258

Fonds de Recherche du Québec-Santé

Grant: 322736

Fonds de Recherche du Québec-Santé

Grant: 324636

Natural Sciences and Engineering Research Council of Canada

Grant: RGPIN-2019-07244

National Institutes of Health Eunice Kennedy Shriver National Institute of Child Health and Development

Grant: R03HD094577

NINDS NIH HHS

Grant: R01 NS109450

Swiss National Science Foundation

Grant: 204934

NINDS NIH HHS

Grant: L30 NS108301

NINDS NIH HHS

Grant: R01 NS128478

NINDS NIH HHS

Grant: K23 NS104211

NICHD NIH HHS

Grant: K01 HD106928

Natural Sciences and Engineering Research Council of Canada

Grant: unidentified

unidentified

Swiss National Science Foundation

Grant: 109450

Designing reliable integrated systems with unreliable components

National Institutes of Health

Grant: 5R01NS109450-05

Characterization of central pain mechanisms using simultaneous spinal cord-brain functional imaging

European Commission

Grant: 101107932

Traumatic Spinal Cord Injury: The Need to Classify Disease Severity

National Institutes of Health

Grant: 5K01HD106928-03

Improving mechanistic understanding of responsiveness to spinal cord stimulation after spinal cord injury

National Institutes of Health

Grant: 5K23NS104211-03

Neuroimaging-Based Brain and Spinal Cord Biomarkers for Cervical Radiculopathy

National Health and Medical Research Council (NHMRC)

Grant: 106928

The role of past sun exposure, infection history and other exogenous factors in Multiple Sclerosis

National Institutes of Health

Grant: 7R03HD094577-03

MRI and machine learning to improve early prognosis and clinical management after spinal cord injury

National Institutes of Health

Grant: 1R01NS128478-01A1

MRI-Derived Neuromuscular Signatures to Predict Surgical Response in Degenerative Cervical Myelopathy

Swiss National Science Foundation

Grant: 214211

Investigating movement-induced corticospinal modulation of pain in fibromyalgia using simultaneous brain and spinal cord functional magnetic resonance imaging

FWCI

1.62

Citation Percentile

0.8%

Citation Trend

2025

2026

hybridLicense: cc-by

journalhttps://doi.org/10.1148/ryai.240005

publisherhttps://doi.org/10.1148/ryai.240005

publisherhttp://pubs.rsna.org/doi/pdf/10.1148/ryai.240005

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39503603

repositoryhttps://publications.polymtl.ca/59871/

Fields of Study

Medical Imaging and AnalysisCervical and Thoracic MyelopathySpinal Fractures and Fixation Techniques02 engineering and technology0202 electrical engineering, electronic engineering, information engineeringHumansSpinal Cord InjuriesMagnetic Resonance ImagingRetrospective StudiesMaleFemaleDeep LearningMiddle AgedAdultImage Interpretation, Computer-AssistedAged

MeSH Terms

Deep LearningAdultAgedFemaleHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesSpinal Cord Injuries

Keywords

MedicineLesionSpinal cordIntramedullary rodMagnetic resonance imagingSagittal planeRadiologySpinal cord injurySegmentationCordLumbarSurgeryArtificial intelligenceComputer scienceTraumaMr ImagingSupervised LearningConvolutional Neural Network (Cnn)MaleAdultNeurosciences610 Medicine & healthMiddle AgedDeep LearningImage Interpretation, Computer-AssistedHumans10046 Balgrist University Hospital, Swiss Spinal Cord Injury CenterFemale610 Medicine & healthSpinal Cord InjuriesOriginal ResearchRetrospective StudiesAged