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arxiv: 2305.07642 · v1 · pith:4PE7NJSBnew · submitted 2023-05-12 · 💻 cs.CV · cs.AI· cs.LG· stat.ML

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
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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
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classification 💻 cs.CV cs.AIcs.LGstat.ML
keywords meningiomawillautomatedchallengemodelssegmentationtumorbrats
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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.

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