Adversarial Inpainting of Medical Image Modalities

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.

arXiv commentTo be submitted to ICASSP 2019

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Conditional Generative Adversarial Nets
• Perceptual Adversarial Networks for Image-to-Image Transformation
• SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting
• Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation

Cited by in corpus (10)

• Generative Adversarial Network in Medical Imaging: A Review
• Image inpainting: A review
• Unsupervised Medical Image Translation Using Cycle-MedGAN
• PCSGAN: Perceptual Cyclic-Synthesized Generative Adversarial Networks for Thermal and NIR to Visible Image Transformation
• ipA-MedGAN: Inpainting of Arbitrary Regions in Medical Imaging
• An Adversarial Super-Resolution Remedy for Radar Design Trade-offs
• Towards Adversarial Denoising of Radar Micro-Doppler Signatures
• Medical Image Generation using Generative Adversarial Networks
• HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis
• Where is the Fake? Patch-Wise Supervised GANs for Texture Inpainting