Unpaired fundus image enhancement based on constrained generative
adversarial networks
Abstract
Fundus photography (FP) is a crucial technique for diagnosing the
progression of ocular and systemic diseases in clinical studies, with
wide applications in early clinical screening and diagnosis. However,
due to the non-uniform illumination and imbalanced intensity caused by
various reasons, the quality of fundus images is often severely
weakened, brings challenges for automated screening, analysis and
diagnosis of diseases. To resolve this problem, we developed strongly
constrained generative adversarial networks (SCGAN). The results
demonstrate that the quality of various datasets were more significantly
enhanced based on SCGAN, simultaneously more effectively retaining
tissue and vascular information under various experimental conditions
. Furthermore, the clinical effectiveness and robustness of this
model was validated by showing its improved ability in vascular
segmentation as well as disease diagnosis. Our study provides a new
comprehensive approach for FP and also possesses the potential capacity
to advance artificial intelligence-assisted ophthalmic examination.