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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月27日

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  • 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,
  • 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,
  • Ulas Bagci,
  • 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 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 H. Abayazeed,
  • Raymond Huang,
  • Maria Correia de Verdier,
  • Jeffrey D. Rudie,
  • Spyridon Bakas,
  • Evan Calabrese

摘要

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.