Structure and Illumination Constrained GAN for Medical Image Enhancement

The development of medical imaging techniques
has greatly supported clinical decision making.
However, poor imaging quality, such as non-uniform illumination
or imbalanced intensity, brings challenges for
automated screening, analysis and diagnosis of diseases.
Previously, bi-directional GANs (e.g., CycleGAN), have been
proposed to improve the quality of input images without
the requirement of paired images. However, these methods
focus on global appearance, without imposing constraints
on structure or illumination, which are essential features
for medical image interpretation. In this paper, we propose
a novel and versatile bi-directional GAN, named Structure
and illumination constrained GAN (StillGAN), for medical
image quality enhancement. Our StillGAN treats low- and
high-quality images as two distinct domains, and introduces
local structure and illumination constraints for learning
both overall characteristics and local details. Extensive
experiments on three medical image datasets (e.g., corneal
confocal microscopy, retinal color fundus and endoscopy
images) demonstrate that our method performs better than
both conventional methods and other deep learning-based
methods. In addition, we have investigated the impact of
the proposed method on different medical image analysis
and clinical tasks such as nerve segmentation, tortuosity
grading, fovea localization and disease classification.