The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023:
  Intracranial Meningioma
Dominic LaBella Maruf Adewole Michelle Alonso-Basanta Talissa Altes Syed Muhammad Anwar Ujjwal Baid Timothy Bergquist Radhika Bhalerao Sully Chen Verena Chung Gian-Marco Conte Farouk Dako James Eddy Ivan Ezhov Devon Godfrey Fathi Hilal Ariana Familiar Keyvan Farahani Juan Eugenio Iglesias Zhifan Jiang Elaine Johanson Anahita Fathi Kazerooni Collin Kent John Kirkpatrick Florian Kofler Koen Van Leemput Hongwei Bran Li Xinyang Liu Aria Mahtabfar Shan McBurney-Lin Ryan McLean Zeke Meier Ahmed W Moawad John Mongan Pierre Nedelec Maxence Pajot Marie Piraud Arif Rashid Zachary Reitman Russell Takeshi Shinohara Yury Velichko Chunhao Wang Pranav Warman Walter Wiggins Mariam Aboian Jake Albrecht Udunna Anazodo Spyridon Bakas Adam Flanders Anastasia Janas Goldey Khanna Marius George Linguraru Bjoern Menze Ayman Nada Andreas M Rauschecker Jeff Rudie Nourel Hoda Tahon Javier Villanueva-Meyer Benedikt Wiestler Evan Calabrese
Meningiomas are the most common primary intracranial tumor in adults and can
be associated with significant morbidity and mortality. Radiologists,
neurosurgeons, neuro-oncologists, and radiation oncologists rely on
multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal
treatment monitoring; yet automated, objective, and quantitative tools for
non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS
meningioma 2023 challenge will provide a community standard and benchmark for
state-of-the-art automated intracranial meningioma segmentation models based on
the largest expert annotated multilabel meningioma mpMRI dataset to date.
Challenge competitors will develop automated segmentation models to predict
three distinct meningioma sub-regions on MRI including enhancing tumor,
non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity.
Models will be evaluated on separate validation and held-out test datasets
using standardized metrics utilized across the BraTS 2023 series of challenges
including the Dice similarity coefficient and Hausdorff distance. The models
developed during the course of this challenge will aid in incorporation of
automated meningioma MRI segmentation into clinical practice, which will
ultimately improve care of patients with meningioma.