Adversarial Inpainting of Medical Image Modalities
This work addresses image quality issues in medical imaging for tasks like attenuation correction and radiation therapy planning, but it is incremental as it adapts existing inpainting methods to medical data.
The paper tackled the problem of partial deteriorations in medical images, such as metallic implants in MRI scans, by proposing a GAN-based inpainting framework that outperformed other techniques on two medical modalities with qualitative and quantitative improvements.
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.