Computer-aided diagnosis based on enhancement of degraded fundus photographs

Digital ray: enhancing cataractous fundus images using style transfer generative adversarial networks to improve retinopathy detection

BACKGROUND/AIMS

The aim of this study was to develop and evaluate digital ray, based on preoperative and postoperative image pairs using style transfer generative adversarial networks (GANs), to enhance cataractous fundus images for improved retinopathy detection.

METHODS

For eligible cataract patients, preoperative and postoperative colour fundus photographs (CFP) and ultra-wide field (UWF) images were captured. Then, both the original CycleGAN and a modified CycleGAN (C2ycleGAN) framework were adopted for image generation and quantitatively compared using Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Additionally, CFP and UWF images from another cataract cohort were used to test model performances. Different panels of ophthalmologists evaluated the quality, authenticity and diagnostic efficacy of the generated images.

RESULTS

A total of 959 CFP and 1009 UWF image pairs were included in model development. FID and KID indicated that images generated by C2ycleGAN presented significantly improved quality. Based on ophthalmologists' average ratings, the percentages of inadequate-quality images decreased from 32% to 18.8% for CFP, and from 18.7% to 14.7% for UWF. Only 24.8% and 13.8% of generated CFP and UWF images could be recognised as synthetic. The accuracy of retinopathy detection significantly increased from 78% to 91% for CFP and from 91% to 93% for UWF. For retinopathy subtype diagnosis, the accuracies also increased from 87%-94% to 91%-100% for CFP and from 87%-95% to 93%-97% for UWF.

CONCLUSION

Digital ray could generate realistic postoperative CFP and UWF images with enhanced quality and accuracy for overall detection and subtype diagnosis of retinopathies, especially for CFP.\ TRIAL REGISTRATION NUMBER: This study was registered with ClinicalTrials.gov (NCT05491798).

Retinal image enhancement based on color dominance of image

Real-time fundus images captured to detect multiple diseases are prone to different quality issues like illumination, noise, etc., resulting in less visibility of anomalies. So, enhancing the retinal fundus images is essential for a better prediction rate of eye diseases. In this paper, we propose Lab color space-based enhancement techniques for retinal image enhancement. Existing research works does not consider the relation between color spaces of the fundus image in selecting a specific channel to perform retinal image enhancement. Our unique contribution to this research work is utilizing the color dominance of an image in quantifying the distribution of information in the blue channel and performing enhancement in Lab space followed by a series of steps to optimize overall brightness and contrast. The test set of the Retinal Fundus Multi-disease Image Dataset is used to evaluate the performance of the proposed enhancement technique in identifying the presence or absence of retinal abnormality. The proposed technique achieved an accuracy of 89.53 percent.

Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives

Background

The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic.

Main text

At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns.

Conclusions

This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.

Enhance Contrast and Balance Color of Retinal Image

This paper proposes a simple and effective retinal fundus image simulation modeling to enhance contrast and adjust the color balance for symmetric information in biomedicine. The aim of the study is for reliable diagnosis of AMD (age-related macular degeneration) screening. The method consists of a few simple steps. Firstly, local image contrast is refined with the CLAHE (Contrast Limited Adaptive Histogram Equalization) technique by operating CIE L*a*b* color space. Then, the contrast-enhanced image is stretched and rescaled by a histogram scaling equation to adjust the overall brightness offsets of the image and standardize it to Hubbard’s retinal image brightness range. The proposed method was assessed with retinal images from the DiaretDB0 and STARE datasets. The findings in the experimentation section indicate that the proposed method results in delightful color naturalness along with a standard color of retinal lesions.

Noninvasive temporal detection of early retinal vascular changes during diabetes

Diabetes associated complications, including diabetic retinopathy and loss of vision, are major health concerns. Detecting early retinal vascular changes during diabetes is not well documented, and only few studies have addressed this domain. The purpose of this study was to noninvasively evaluate temporal changes in retinal vasculature at very early stages of diabetes using fundus images from preclinical models of diabetes. Non-diabetic and Akita/+ male mice with different duration of diabetes were subjected to fundus imaging using a Micron III imaging system. The images were obtained from 4 weeks- (onset of diabetes), 8 weeks-, 16 weeks-, and 24 weeks-old male Akita/+ and non-diabetic mice. In total 104 fundus images were subjected to analysis for various feature extractions. A combination of Canny Edge Detector and Angiogenesis Analyzer plug-ins in ImageJ were utilized to quantify various retinal vascular changes in fundus images. Statistical analyses were conducted to determine significant differences in the various extracted features from fundus images of diabetic and non-diabetic animals. Our novel image analysis method led to extraction of over 20 features. These results indicated that some of these features were significantly changed with a short duration of diabetes, and others remained the same but changed after longer duration of diabetes. These patterns likely distinguish acute (protective) and chronic (damaging) associated changes with diabetes. We show that with a combination of various plugging one can extract over 20 features from retinal vasculature fundus images. These features change during diabetes, thus allowing the quantification of quality of retinal vascular architecture as biomarkers for disease progression. In addition, our method was able to identify unique differences among diabetic mice with different duration of diabetes. The ability to noninvasively detect temporal retinal vascular changes during diabetes could lead to identification of specific markers important in the development and progression of diabetes mediated-microvascular changes, evaluation of therapeutic interventions, and eventual reversal of these changes in order to stop or delay disease progression.

Enhancement of blurry retinal image based on non-uniform contrast stretching and intensity transfer

Proper contrast and sufficient illuminance are important in clearly identifying the retinal structures, while the required quality cannot always be guaranteed due to major reasons like acquisition process and diseases. To ensure the effectiveness of enhancement, two solutions are developed for blurry retinal images with sufficient illuminance and insufficient illuminance, respectively. The proposed contrast stretching and intensity transfer are main steps in both of the two solutions. The contrast stretching is based on base-intensity removal and non-uniform addition. We assume that a base-intensity exists in an image, which mainly supports the basic illuminance but has less contribution to texture information. The base-intensity is estimated by the constrained Gaussian function and then removed. The non-uniform addition using compressed Gamma map is further developed to improve the contrast. Additionally, an effective intensity transfer strategy is introduced, which can provide required illuminance for a single channel after contrast stretching. The color correction can be achieved if the intensity transfer is performed on three channels. Results show that the proposed solutions can effectively improve the contrast and illuminance, and good visual perception for quality degraded retinal images is obtained. Illustration of contrast stretching based on a signal colour channel.