Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation
<|reference_start|>Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation: The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and Computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes.<|reference_end|>
@article{labella2024brain,
    title={Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy
  Planning Automated Segmentation},
    author={Dominic LaBella, Katherine Schumacher, Michael Mix, Kevin Leu, Shan
  McBurney-Lin, Pierre Nedelec, Javier Villanueva-Meyer, Jonathan Shapey, Tom
  Vercauteren, Kazumi Chia, Omar Al-Salihi, Justin Leu, Lia Halasz, Yury
  Velichko, Chunhao Wang, John Kirkpatrick, Scott Floyd, Zachary J. Reitman,
  Trey Mullikin, Ulas Bagci, Sean Sachdev, Jona A. Hattangadi-Gluth, Tyler
  Seibert, Nikdokht Farid, Connor Puett, Matthew W. Pease, Kevin Shiue, Syed
  Muhammad Anwar, Shahriar Faghani, Muhammad Ammar Haider, Pranav Warman, Jake
  Albrecht, Andr'as Jakab, Mana Moassefi, Verena Chung, Alejandro Aristizabal,
  Alexandros Karargyris, Hasan Kassem, Sarthak Pati, Micah Sheller, Christina
  Huang, Aaron Coley, Siddharth Ghanta, Alex Schneider, Conrad Sharp, Rachit
  Saluja, Florian Kofler, Philipp Lohmann, Phillipp Vollmuth, Louis Gagnon,
  Maruf Adewole, Hongwei Bran Li, Anahita Fathi Kazerooni, Nourel Hoda Tahon,
  Udunna Anazodo, Ahmed W. Moawad, Bjoern Menze, Marius George 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},
    journal={arXiv preprint arXiv:2405.18383},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18383},
    primaryClass={cs.CV cs.AI cs.HC cs.LG}
}