Image super-resolution using progressive generative adversarial networks for medical image analysis.

Anatomical landmark segmentation and pathology localisation are important steps in automated analysis of medical images. They are particularly challenging when the anatomy or pathology is small, as in retinal images (e.g. vasculature branches or microaneurysm lesions) 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-GANs) 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 pathologies. Our primary contribution is in proposing a multi-stage 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 multi stage P-GAN outperforms competing methods and baseline GANs. The super resolved images when used for landmark and pathology detection result in accuracy levels close to those obtained when using the original high resolution images. We also demonstrate our methods effectiveness on magnetic resonance (MR) images, thus establishing its broader applicability.