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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.