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

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

in 639/705/1046 / 692/308/575 / 692/700/1421/65 / Automation / Brain cancer / Brain Neoplasms - diagnostic imaging / Brain Neoplasms - radiotherapy / Brain tumors / Data Descriptor / Datasets / Human subjects / Humanities and Social Sciences / Humans / Image processing / Magnetic Resonance Imaging / Meningeal Neoplasms - diagnostic imaging / Meningeal Neoplasms - radiotherapy / Meningioma / Meningioma - diagnostic imaging / Meningioma - radiotherapy / Metadata / multidisciplinary / Patients / Radiation therapy / Radiotherapy Planning, Computer-Assisted / Science / Science (multidisciplinary) / Segmentation / Tomography / Tumors

2026

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

2026

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2026

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Journal Article

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

Vercauteren, Tom,

Vollmuth, Phillipp,

Albrecht, Jake,

Lohmann, Philipp,

Calabrese, Evan,

Barfoot, Theodore,

Tahon, Nourel H.,

Al-Salihi, Omar,

Moassefi, Mana,

Wiestler, Benedikt,

LaBella, Dominic,

Nedelec, Pierre,

Anwar, Syed M.,

Chung, Verena,

Adewole, Maruf,

de Verdier, Maria Correia,

Schumacher, Katherine,

Kazerooni, Anahita Fathi,

Chia, Kazumi,

Menze, Bjoern,

Leu, Justin,

Sheller, Micah,

Wang, Chunhao,

Kirkpatrick, John P.,

Maleki, Nazanin,

Chai, Rong,

Raleigh, David R.,

Farid, Nikdokht,

Pease, Matthew W.,

Hongwei B, Li,

Mullikin, Trey C.,

Sachdev, Sean,

Villanueva-Meyer, Javier,

Ivory, Marina,

Halasz, Lia M.,

Linguraru, Marius G.,

Bagci, Ulas,

Reitman, Zachary J.,

Floyd, Scott R.,

Abayazeed, Aly H.,

Schwarz, Christopher G.,

Leu, Kevin,

Puett, Connor,

Baid, Ujjwal,

Saluja, Rachit,

Taylor, Peter,

Faghani, Shahriar,

Shapey, Jonathan,

Velichko, Yury,

Hattangadi-Gluth, Jona A.,

Karargyris, Alexandros,

Seibert, Tyler M.,

Moawad, Ahmed W.,

Gagnon, Louis,

Aristizabal, Alejandro,

Vaios, Eugene J.,

Kofler, Florian,

McBurney-Lin, Shan,

Warman, Pranav,

Jakab, András,

Aboian, Mariam,

Bakas, Spyridon,

Kassem, Hasan,

2026

Overview

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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Publisher

Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio

Subject

/ Brain Neoplasms - diagnostic imaging

/ Brain Neoplasms - radiotherapy