  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.
