ipA-MedGAN: Inpainting of Arbitrary Regions in Medical Imaging

TL;DR

ipA-MedGAN is a novel generative framework that effectively inpaints arbitrary regions in medical images, especially brain MR scans, without prior localization, improving post-processing tasks.

Contribution

The paper introduces ipA-MedGAN, a new inpainting method capable of handling arbitrary-shaped regions without prior localization, surpassing previous approaches.

Findings

Outperforms existing inpainting methods in qualitative and quantitative metrics.

Effectively handles arbitrary-shaped missing regions in brain MRI.

Enhances subsequent image analysis tasks like segmentation and classification.

Abstract

Local deformations in medical modalities are common phenomena due to a multitude of factors such as metallic implants or limited field of views in magnetic resonance imaging (MRI). Completion of the missing or distorted regions is of special interest for automatic image analysis frameworks to enhance post-processing tasks such as segmentation or classification. In this work, we propose a new generative framework for medical image inpainting, titled ipA-MedGAN. It bypasses the limitations of previous frameworks by enabling inpainting of arbitrary shaped regions without a prior localization of the regions of interest. Thorough qualitative and quantitative comparisons with other inpainting and translational approaches have illustrated the superior performance of the proposed framework for the task of brain MR inpainting.