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LaBella, D.* ; Khanna, O.* ; McBurney-Lin, S.* ; McLean, R.M.* ; Nedelec, P.* ; Rashid, A.S.* ; Tahon, N.H.* ; Altes, T.* ; Baid, U.* ; Bhalerao, R.P.* ; Dhemesh, Y.* ; Floyd, S.* ; Godfrey, D.I.* ; Hilal, F.* ; Janas, A.* ; Kazerooni, A.* ; Kent, C.* ; Kirkpatrick, J.* ; Kofler, F. ; Leu, K.* ; Maleki, N.* ; Menze, B.* ; Pajot, M.* ; Reitman, Z.J.* ; Rudie, J.D.* ; Saluja, R.* ; Velichko, Y.* ; Wang, C.* ; Warman, P.I.* ; Sollmann, N.* ; Diffley, D.* ; Nandolia, K.K.* ; Warren, D.I.* ; Hussain, A.* ; Fehringer, J.P.* ; Bronstein, Y.* ; Deptula, L.* ; Stein, E.G.* ; Taherzadeh, M.* ; Portela de Oliveira, E.* ; Haughey, A.* ; Kontzialis, M.* ; Saba, L.* ; Turner, B.M.* ; Brüßeler, M.M.T.* ; Ansari, S.* ; Gkampenis, A.* ; Weiss, D.M.* ; Mansour, A.* ; Shawali, I.H.* ; Yordanov, N.* ; Stein, J.M.* ; Hourani, R.* ; Moshebah, M.Y.* ; Abouelatta, A.M.* ; Rizvi, T.* ; Willms, K.* ; Martin, D.C.* ; Okar, A.* ; D'Anna, G.* ; Taha, A.* ; Sharifi, Y.* ; Faghani, S.* ; Kite, D.* ; Pinho, M.* ; Haider, M.A.* ; Alonso-Basanta, M.* ; Villanueva-Meyer, J.* ; Rauschecker, A.M.* ; Nada, A.* ; Aboian, M.* ; Flanders, A.* ; Bakas, S.* ; Calabrese, E.*

A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Sci. Data 11:496 (2024)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.
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Publication type Article: Journal article
Document type Scientific Article
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 2052-4463
e-ISSN 2052-4463
Journal Scientific Data
Quellenangaben Volume: 11, Issue: 1, Pages: , Article Number: 496 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-530001-001
Grants National Institutes of Health (NIH)
American Society of Neuroradiology (ASNR)
Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
DOI 10.1038/s41597-024-03350-9
WOS ID 001224183500006
Scopus ID 85193323305
PubMed ID 38750041
Erfassungsdatum 2024-05-17
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