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Adversarial Inpainting of Medical Image Modalities

2018-10-15 19:14:16
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang
arXiv_CV
             
Abstract

Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of medical images via Generative Adversarial Networks (GANs). The proposed framework incorporates two patch-based discriminator networks with additional style and perceptual losses for the inpainting of missing information in realistically detailed and contextually consistent manner. The proposed framework outperformed other natural image inpainting techniques both qualitatively and quantitatively on two different medical modalities.

Abstract (translated)

URL

https://arxiv.org/abs/1810.06621

PDF

https://arxiv.org/pdf/1810.06621.pdf

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