Progressive Generative Adversarial Networks for Medical Image Super resolution

TL;DR

This paper introduces a multistage progressive GAN approach for medical image super-resolution, improving image quality stepwise to enhance landmark detection and pathology localization.

Contribution

The paper proposes a novel multistage P-GAN model with triplet loss for progressive image quality enhancement in medical super-resolution tasks.

Findings

Outperforms baseline GAN and competing methods in super-resolution quality

Enables high scaling factor image generation with maintained quality

Improves accuracy in medical landmark and pathology detection

Abstract

Anatomical landmark segmentation and pathology localization are important steps in automated analysis of medical images. They are particularly challenging when the anatomy or pathology is small, as in retinal images and cardiac MRI, or when the image is of low quality due to device acquisition parameters as in magnetic resonance (MR) scanners. We propose an image super-resolution method using progressive generative adversarial networks (P-GAN) that can take as input a low-resolution image and generate a high resolution image of desired scaling factor. The super resolved images can be used for more accurate detection of landmarks and pathology. Our primary contribution is in proposing a multistage model where the output image quality of one stage is progressively improved in the next stage by using a triplet loss function. The triplet loss enables stepwise image quality improvement by using the output of the previous stage as the baseline. This facilitates generation of super resolved images of high scaling factor while maintaining good image quality. Experimental results for image super-resolution show that our proposed multistage P-GAN outperforms competing methods and baseline GAN.