Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review

Diabetic Macular Edema (DME) is a potentially blinding consequence of Diabetic Retinopa-thy (DR) as well as the leading cause of vision loss in diabetics. DME is characterized by a buildup of extracellular fluid inside the macula through hyperpermeable vessels. The pres-ence of DME can be spotted at any level of DR with varying degrees of severity using promi-nent imaging modalities such as Color Fundus Photography (CFP) and Optical Coherence Tomography (OCT). Computerized approaches for screening eye disorders appear to be beneficial, as they provide doctors with detailed insights into abnormalities. Such a system for the evaluation of retinal images can function as a stand-alone disease monitoring sys-tem. This review reports the state-of-art automated DME detection methods with tradi-tional Machine Learning (ML) and Deep Learning (DL) techniques employing retinal fundus or OCT images. The paper provides a list of public retinal OCT and fundus imaging datasets for DME detection. In addition, the paper describes the dynamics of advancements in presented methods adopted in the past along with their strengths and limitations to highlight the insufficiencies that could be addressed in future investigations. (c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Automated summary

Diabetic Macular Edema (DME) is a serious complication of Diabetic Retinopathy (DR) and it is the leading cause of vision loss in diabetics. DME is characterized by the accumulation of fluid in the macula due to leaky blood vessels. Advanced imaging techniques such as Color Fundus Photography (CFP) and Optical Coherence Tomography (OCT) can detect the presence of DME at different stages of DR. This review article discusses the latest automated DME detection methods using traditional Machine Learning (ML) and Deep Learning (DL) techniques with retinal fundus or OCT images.