LaBella Dominic, Adewole Maruf, Alonso-Basanta Michelle, Altes Talissa, Anwar Syed Muhammad, Baid Ujjwal, Bergquist Timothy, Bhalerao Radhika, Chen Sully, Chung Verena, Conte Gian-Marco, Dako Farouk, Eddy James, Ezhov Ivan, Godfrey Devon, Hilal Fathi, Familiar Ariana, Farahani Keyvan, Iglesias Juan Eugenio, Jiang Zhifan, Johanson Elaine, Kazerooni Anahita Fathi, Kent Collin, Kirkpatrick John, Kofler Florian, Leemput Koen Van, Li Hongwei Bran, Liu Xinyang, Mahtabfar Aria, McBurney-Lin Shan, McLean Ryan, Meier Zeke, Moawad Ahmed W, Mongan John, Nedelec Pierre, Pajot Maxence, Piraud Marie, Rashid Arif, Reitman Zachary, Shinohara Russell Takeshi, Velichko Yury, Wang Chunhao, Warman Pranav, Wiggins Walter, Aboian Mariam, Albrecht Jake, Anazodo Udunna, Bakas Spyridon, Flanders Adam, Janas Anastasia, Khanna Goldey, Linguraru Marius George, Menze Bjoern, Nada Ayman, Rauschecker Andreas M, Rudie Jeff, Tahon Nourel Hoda, Villanueva-Meyer Javier, Wiestler Benedikt, Calabrese Evan
ArXiv. 2023 May 12:arXiv:2305.07642v1.
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.
脑膜瘤是成人最常见的原发性颅内肿瘤，可导致显著的发病率和死亡率。放射科医生、神经外科医生、神经肿瘤学家和放射肿瘤学家依靠多参数MRI（mpMRI）进行诊断、治疗规划和长期治疗监测；然而，缺乏用于在mpMRI上对脑膜瘤进行非侵入性评估的自动化、客观和定量工具。2023年BraTS脑膜瘤挑战赛将基于迄今为止最大的专家标注多标签脑膜瘤mpMRI数据集，为最先进的自动化颅内脑膜瘤分割模型提供社区标准和基准。挑战赛参赛者将开发自动化分割模型，以预测MRI上三个不同的脑膜瘤子区域，包括强化肿瘤、非强化肿瘤核心和周围非强化T2/FLAIR高信号。将使用2023年BraTS系列挑战赛中使用的标准化指标，包括骰子相似系数和豪斯多夫距离，在单独的验证和保留测试数据集上对模型进行评估。在本次挑战赛过程中开发的模型将有助于将自动化脑膜瘤MRI分割纳入临床实践，这最终将改善脑膜瘤患者的护理。
