  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.
