CT-To-MR Conditional Generative Adversarial Networks for Improved Stroke Lesion Segmentation

Jonathan Rubin, S. Mazdak Abulnaga

Keywords: Conditional adversarial networks, Image-to-Image translation, Ischemic stroke lesion segmentation, CT perfusion

TL;DR: CT-To-MR Conditional GAN for Stroke Lesion Segmentation

Abstract: Infarcted brain tissue resulting from acute stroke readily shows up as hyperintense regions within diffusion-weighted magnetic resonance imaging (DWI). It has also been proposed that computed tomography perfusion (CTP) could alternatively be used to triage stroke patients, given improvements in speed and availability, as well as reduced cost. However, CTP has a lower signal to noise ratio compared to MR. In this work, we investigate whether a conditional mapping can be learned by a generative adversarial network to map CTP inputs to generated MR DWI that more clearly delineates hyperintense regions due to ischemic stroke. We detail the architectures of the generator and discriminator and describe the training process used to perform image-to-image translation from multi-modal CT perfusion maps to diffusion weighted MR outputs. We evaluate the results both qualitatively by visual comparison of generated MR to ground truth, as well as quantitatively by training fully convolutional neural networks that make use of generated MR data inputs to perform ischemic stroke lesion segmentation. We show that segmentation networks trained with generated CT-to-MR inputs are able to outperform networks that make use of only CT perfusion input.

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