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Abstract #1894

Automated Segmentation of Brain Meningioma MRIs with Generative Adversarial Networks

Agata Sularz1, Fulvio Zaccagna1, Dimitri A Kessler1, Fraser Tonnard1, Sonia Benitez1, Thomas Santarius2, Fiona J Gilbert1, Tomasz Matys1, and Joshua D Kaggie1
1Department of Radiology, University of Cambridge, Cambridge, United Kingdom, 2Department of Neurosurgery, University of Cambridge, Cambridge, United Kingdom

We describe a deep learning method for fully-automated brain meningioma MRI segmentation. A conditional generative adversarial network (cGAN) was trained on T1 contrast-enhanced (T1ce) MRI of 37 patients. We explored the effect of batch size, transfer learning and histogram equalization on segmentation accuracy. The highest results for T1ce images were achieved for meningioma dataset of batch size = 1 (DSC = 0.347). Histogram equalization improved segmentation accuracy for batch size = 1 (DSC = 0.364) and batch size = 200. Transfer learning on a publicly available glioma dataset did not improve segmentation results.

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