Progressive Generative Adversarial Networks for Medical Image Super resolution
This work addresses the challenge of small anatomy or low-quality images in medical imaging, such as in retinal images and cardiac MRI, by enhancing super-resolution for more accurate detection, though it appears incremental as it builds on existing GAN methods.
The authors tackled the problem of medical image super-resolution for improved anatomical landmark segmentation and pathology localization by proposing a progressive generative adversarial network (P-GAN) with a triplet loss, resulting in outperforming competing methods and baseline GAN in experimental results.
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.