The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) dataset

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Authors: Dominic LaBella, Katherine Schumacher, Michael Mix, Kevin Leu, Shan McBurney-Lin, Pierre Nedelec, Javier Villanueva‐Meyer, David R. Raleigh, Jonathan Shapey, Tom Vercauteren, Kazumi Chia, Marina Ivory
+60 more Theodore Barfoot, Omar Al-Salihi, Justin Leu, Lia M. Halasz, Yury Velichko, Chunhao Wang, John P. Kirkpatrick, Scott R. Floyd, Zachary J. Reitman, Trey C. Mullikin, Eugene J. Vaios, Ulaş Bağcı, Sean Sachdev, Jona A. Hattangadi-Gluth, Tyler M. Seibert, Nikdokht Farid, Connor Puett, Matthew W. Pease, Kevin Shiue, Syed M. Anwar, Shahriar Faghani, Peter Taylor, Pranav I. Warman, Jake Albrecht, András Jakab, Mana Moassefi, Verena Chung, Rong Chai, Alejandro Aristizabal, Alexandros Karargyris, Hasan Kassem, Sarthak Pati, Micah Sheller, Nazanin Maleki, Rachit Saluja, Florian Kofler, Christopher G. Schwarz, Philipp Lohmann, Phillipp Vollmuth, Louis Gagnon, Maruf Adewole, Li Hongwei B, Anahita Fathi Kazerooni, Nourel H. Tahon, Udunna Anazodo, Ahmed W. Moawad, Bjoern Menze, Marius G. Linguraru, Mariam Aboian, Benedikt Wiestler, Ujjwal Baid, Gian-Marco Conte, Andreas M. Rauschecker, Ayman Nada, Aly Abayazeed, Raymond Huang, Maria Correia de Verdier, Jeffrey D. Rudie, Spyridon Bakas, Evan Calabrese
Cited by: 2
References: 33

Abstract

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.

Fields of Study & Subjects

Advanced Radiotherapy Techniques Meningioma and schwannoma management Brain Metastases and Treatment

Publication details

Journal / source
Scientific Data
Publisher
Springer Science and Business Media LLC
Volume
13
Issue
1
Publication date
27 January 2026
Language
EN