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Towards Longitudinal Glioma Segmentation: Evaluating combined pre- and post-treatment MRI training data for automated tumor segmentation using nnU-Net

medRxiv(2023)10.1101/2023.05.31.23290537Source: DataRank Database

Towards Longitudinal Glioma Segmentation: Evaluating combined pre- and post-treatment MRI training data for automated tumor segmentation using nnU-Net is a research paper published in medRxiv (2023). On theSindex it has a DataRank of 0. It has been cited 3 times.

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Open Access3 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (7 grants)OA: greenTopics (18)IDs (PubMed, OpenAlex)

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Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (9)

Kyle Singleton ORCID,Lee Curtin ORCID,Lisa E. Paulson ORCID,Kamala Clark-Swanson ORCID,Andrea Hawkins‐Daarud ORCID

Live enrichment

PubMed:37333148 OpenAlex:https://openalex.org/W4379380514

NCI NIH HHS

Grant: R01 CA164371

NINDS NIH HHS

Grant: R01 NS060752

NCI NIH HHS

Grant: U54 CA143970

NCI NIH HHS

Grant: U01 CA250481

NCI NIH HHS

Grant: U54 CA210180

NCI NIH HHS

Grant: U54 CA193489

NCI NIH HHS

Grant: U01 CA220378

greenLicense: cc-by-nc-nd

repositoryhttps://www.medrxiv.org/content/medrxiv/early/2023/06/05/2023.05.31.23290537.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.05.31.23290537

repositoryhttps://doi.org/10.1101/2023.05.31.23290537

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37333148

Fields of Study

Glioma Diagnosis and TreatmentRadiomics and Machine Learning in Medical ImagingBrain Tumor Detection and Classification

Keywords

SegmentationConvolutional neural networkComputer scienceMagnetic resonance imagingWorkflowGliomaArtificial intelligenceMarket segmentationMedicinePattern recognition (psychology)RadiologyDatabaseGlioma SegmentationMRIpost-treatment