Volumetric assessment of meningiomas plays an instrumental role in primary assessment and detection of tumor growth. We used a specially trained deep-learning-model on multiparametric MR-data of 116 patients to evaluate performance in automated-segmentation. The deep-learning-model was trained on 249 gliomas, then further adapted by a subgroup of our meningioma patients (n=60). A second group of meningiomas (n=56) was used for testing performance of the deep-learning-model compared to manual-segmentations. The automated-segmentations showed strong correlation to the manual-segmentations: dice-coefficients were 0.87±0.15 for contrast-enhancing-tumor in T1CE and 0.82±0.12 for total-tumor-volume (union of contrast-enhancing-tumor and edema). Automated-segmentation yielded accurate results comparable to manual interreader-variabilities.
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