Adversarial Inpainting of Medical Image Modalities

TL;DR

This paper introduces a GAN-based inpainting method for medical images that effectively restores missing or corrupted regions, improving post-processing tasks in medical imaging.

Contribution

It presents a novel GAN framework with patch-based discriminators and style/perceptual losses tailored for medical image inpainting, outperforming existing natural image methods.

Findings

Outperforms other inpainting techniques qualitatively and quantitatively

Effective on multiple medical imaging modalities

Produces realistic, contextually consistent restorations

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.