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

Dominic LaBella, Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA. dominic.labella@duke.edu.
Katherine Schumacher, Department of Radiation Oncology, SUNY Upstate Medical University, Syracuse, NY, USA.
Michael Mix, Department of Radiation Oncology, SUNY Upstate Medical University, Syracuse, NY, USA.
Kevin Leu, Center for Intelligent Imaging (ci2), Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, USA.
Shan McBurney-Lin, Center for Intelligent Imaging (ci2), Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, USA.
Pierre Nedelec, Center for Intelligent Imaging (ci2), Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, USA.
Javier Villanueva-Meyer, Center for Intelligent Imaging (ci2), Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, USA.
David R. Raleigh, Departments of Radiation Oncology, Neurological Surgery, and Pathology, University of California San Francisco (UCSF), San Francisco, CA, USA.
Jonathan Shapey, Department of Neurosurgery, King's College Hospital, London, UK.
Tom Vercauteren, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Kazumi Chia, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Marina Ivory, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Theodore Barfoot, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Omar Al-Salihi, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Justin Leu, Department of Radiation Oncology, University of Washington/Fred Hutchinson Cancer Center, Seattle, WA, USA.
Lia M. Halasz, Department of Radiation Oncology, University of Washington/Fred Hutchinson Cancer Center, Seattle, WA, USA.
Yury Velichko, Department of Radiology, Northwestern University, Evanston, IL, USA.
Chunhao Wang, Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
John P. Kirkpatrick, Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
Scott R. Floyd, Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
Zachary J. Reitman, Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
Trey C. Mullikin, Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
Eugene J. Vaios, Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
Ulas Bagci, Department of Radiation Oncology, Northwestern University, Evanston, IL, USA.
Sean Sachdev, Department of Radiology, Northwestern University, Evanston, IL, USA.
Jona A. Hattangadi-Gluth, Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
Tyler M. Seibert, Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
Nikdokht Farid, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Connor Puett, Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
Matthew W. Pease, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
Kevin Shiue, Department of Radiation Oncology, Indiana University, Indianapolis, IN, USA.
Syed M. Anwar, Children's National Hospital, Washington, DC, USA.

Document Type

Journal Article

Publication Date

1-27-2026

Journal

Scientific data

DOI

10.1038/s41597-026-06649-x

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.

Department

Radiology

Link to Full Text

Share

COinS