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Abstract #1845

Clinical evaluation of automated deep-learning based meningioma segmentation in multiparametric MRI

Kai Laukamp1, Lenhard Pennig1, Frank Thiele1, Robert Reimer1, Lukas Goertz1, David Zopfs1, Georgy Shakirin1, Marco Timmer1, Michael Perkuhn1, and Jan Borggrefe1
1UKK, Cologne, Germany

We trained an established deep-learning-model architecture (3D-Deep-Convolutional-Neural-Network, DeepMedic) on manual segmentations from 70 meningiomas independently segmented by two radiologists. The trained deep-learning model was then validated in a group of 55 meningiomas. Ground truth segmentations were established by two further radiologists in a consensus reading. In the validation-group the comparison of the automated deep-learning-model and manual segmentations revealed average dice-coefficients of 0.91±0.08 for contrast-enhancing-tumor volume and 0.82±0.12 for total-lesion-volume. In the training-group, interreader-variabilities of the two manual readers were 0.92±0.07 for contrast-enhancing-tumor and 0.88±0.05 for total-lesion-volume. Deep-learning based automated segmentation yielded high segmentation accuracy, comparable to manual interreader-variability.

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