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Abstract #3081

Fully automated segmentation of meningiomas using a specially trained deep-learning-model on multiparametric MRI

Kai Roman Laukamp1,2,3, Frank Thiele1,4, Lenhard Pennig1, Robert Reimer1, Georgy Shakirin1,4, David Zopfs1, Simon Lennartz1, Marco Timmer5, David Maintz1, Michael Perkuhn1,4, and Jan Borggrefe1

1Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany, 2Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States, 3Radiology, Case Western Reserve University, Cleveland, OH, United States, 4Philips Research Europe, Aachen, Germany, 5Neurosurgery, University Hospital Cologne, Cologne, Germany

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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