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LaBella, D.* ; Schumacher, K.* ; Mix, M.* ; Leu, K.* ; McBurney-Lin, S.* ; Nedelec, P.* ; Villanueva-Meyer, J.* ; Raleigh, D.R.* ; Shapey, J.* ; Vercauteren, T.* ; Chia, K.* ; Ivory, M.* ; Barfoot, T.* ; Al-Salihi, O.* ; Leu, J.* ; Halasz, L.M.* ; Velichko, Y.* ; Wang, C.* ; Kirkpatrick, J.P.* ; Floyd, S.R.* ; Reitman, Z.J.* ; Mullikin, T.C.* ; Vaios, E.J.* ; Bagci, U.* ; Sachdev, S.* ; Hattangadi-Gluth, J.A.* ; Seibert, T.M.* ; Farid, N.* ; Puett, C.* ; Pease, M.W.* ; Shiue, K.* ; Anwar, S.M.* ; Faghani, S.* ; Taylor, P.* ; Warman, P.* ; Albrecht, J.* ; Jakab, A.* ; Moassefi, M.* ; Chung, V.* ; Chai, R.* ; Aristizabal, A.* ; Karargyris, A.* ; Kassem, H.* ; Pati, S.* ; Sheller, M.* ; Maleki, N.* ; Saluja, R.* ; Kofler, F. ; Schwarz, C.G.* ; Lohmann, P.* ; Vollmuth, P.* ; Gagnon, L.* ; Adewole, M.* ; Hongwei B, L.* ; Kazerooni, A.F.* ; Tahon, N.H.* ; Anazodo, U.* ; Moawad, A.W.* ; Menze, B.* ; Linguraru, M.G.* ; Aboian, M.* ; Wiestler, B.* ; Baid, U.* ; Conte, G.M.* ; Rauschecker, A.M.* ; Nada, A.* ; Abayazeed, A.H.* ; Huang, R.* ; de Verdier, M.C.* ; Rudie, J.D.* ; Bakas, S.* ; Calabrese, E.*

The 2024 brain tumor segmentation challenge meningioma radiotherapy (BraTS-MEN-RT) dataset.

Sci. Data 13:306 (2026)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Meningiomas are the most common primary intracranial tumors, frequently requiring radiotherapy as a part of management. Effective radiotherapy planning for meningiomas necessitates accurate and consistent segmentation of target volumes on MRI, a process that is complex, labor-intensive, and dependent on expert expertise. The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) Dataset addresses this problem by providing the largest multi-institutional collection of systematically annotated radiotherapy planning MRIs for meningiomas. Publicly accessible, this dataset comprises 570 radiotherapy planning 3D T1-weighted post-contrast MRIs at native resolutions, with 500 cases featuring expert-annotated gross tumor volumes (GTV). Annotations follow standardized radiotherapy planning protocols and include both intact and postoperative meningioma cases, ensuring wide clinical relevance. Contributions from seven diverse medical centers across the United States and the United Kingdom enhance the dataset's generalizability. The dataset aims to accelerate the development of automated segmentation methods for radiotherapy planning, improving workflow efficiency, reducing interobserver variability, and ultimately enhancing patient outcomes.
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Publication type Article: Journal article
Document type Software description
Keywords Radiation Therapy ; Meningioma ; Segmentation ; Workflow ; Radiation Treatment Planning ; Brain Tumor; Visualization; Software
ISSN (print) / ISBN 2052-4463
e-ISSN 2052-4463
Journal Scientific Data
Quellenangaben Volume: 13, Issue: 1, Pages: , Article Number: 306 Supplement: ,
Publisher Springer
Publishing Place London
Reviewing status Peer reviewed
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Grants Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)