Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
arxiv(2024)
摘要
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
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
1 compartment voxel abutting the edge of the skull-stripped image edge, which
requires further investigation into optimal pre-processing face anonymization
steps.
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