We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.

翻译：我们描述了BraTS 2023颅内脑膜瘤分割挑战的设计与结果。此次脑膜瘤挑战与此前的BraTS胶质瘤挑战不同，其聚焦于脑膜瘤——一种通常为良性的轴外肿瘤，具有多样的放射学与解剖学表现，且易出现多发性。九个参赛团队利用迄今为止最大的、多机构系统性专家标注的多标签多序列脑膜瘤MRI数据集（包含1000例训练集、141例验证集和283例隐藏测试集）开发了深度学习自动分割模型。每例数据包含T2、T2/FLAIR、T1和T1Gd脑部MRI序列，并附带肿瘤分区标签，分别标注增强肿瘤、非增强肿瘤及周围非增强T2/FLAIR高信号区域。参赛者的自动分割模型基于评分系统进行评估与排名，该系统评估病灶级指标，包括Dice相似系数（DSC）和95% Hausdorff距离。排名第一的团队在增强肿瘤、肿瘤核心和全肿瘤区域的病灶级中位Dice相似系数（DSC）分别为0.976、0.976和0.964，对应的平均DSC分别为0.899、0.904和0.871。这些结果可作为未来术前脑膜瘤自动分割算法的先进基准。此外，我们发现1424例中有1286例（占90.3%）至少有一个分区体素与颅骨剥离图像边缘相邻，这提示需进一步研究最佳预处理面部匿名化步骤。