Early Diagnosis Of Diabetic Retinopathy Research Articles

Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images

This study proposes a novel computer assisted diagnostic (CAD) system for early diagnosis of diabetic retinopathy (DR) using optical coherence tomography (OCT) B-scans. The CAD system is based on fusing novel OCT markers that describe both the morphology/anatomy and the reflectivity of retinal layers to improve DR diagnosis. This system separates retinal layers automatically using a segmentation approach based on an adaptive appearance and their prior shape information. High-order morphological and novel reflectivity markers are extracted from individual segmented layers. Namely, the morphological markers are layer thickness and tortuosity while the reflectivity markers are the 1st-order reflectivity of the layer in addition to local and global high-order reflectivity based on Markov-Gibbs random field (MGRF) and gray-level co-occurrence matrix (GLCM), respectively. The extracted image-derived markers are represented using cumulative distribution function (CDF) descriptors. The constructed CDFs are then described using their statistical measures, i.e., the 10th through 90th percentiles with a 10% increment. For individual layer classification, each extracted descriptor of a given layer is fed to a support vector machine (SVM) classifier with a linear kernel. The results of the four classifiers are then fused using a backpropagation neural network (BNN) to diagnose each retinal layer. For global subject diagnosis, classification outputs (probabilities) of the twelve layers are fused using another BNN to make the final diagnosis of the B-scan. This system is validated and tested on 130 patients, with two scans for both eyes (i.e. 260 OCT images), with a balanced number of normal and DR subjects using different validation metrics: 2-folds, 4-folds, 10-folds, and leave-one-subject-out (LOSO) cross-validation approaches. The performance of the proposed system was evaluated using sensitivity, specificity, F1-score, and accuracy metrics. The system’s performance after the fusion of these different markers showed better performance compared with individual markers and other machine learning fusion methods. Namely, it achieved 96.15%, 99.23%, 97.66%, and 97.69%, respectively, using the LOSO cross-validation technique. The reported results, based on the integration of morphology and reflectivity markers and by using state-of-the-art machine learning classifications, demonstrate the ability of the proposed system to diagnose the DR early.

Use of artificial intelligence for management and identification of complications in diabetes

Artificial intelligence (AI) can play an important role is in early diagnosis of complications, adherence to a healthy lifestyle and medication, real-time monitoring for optimal glycemic status and predictive prognostic model for the diabetic status of a patient. The early recognition and management of the complications (acute as well as chronic) in diabetes predict the quality of life (QoL) of a patient. The promising results of AI in the early diagnosis of diabetic retinopathy have opened the frontiers for management of other complications as well. Although flash glucose monitoring (FGMs) and continuous glucose monitoring(CGMs) are yet to be used in routine clinical practice but these modalities do hold promise in future for management of diabetes. Automated diagnosis of diabetic retinopathy (DR) and cardiovascular risk factor monitoring are now possible based on the large retinal fundus imag-ing datasets with improved sensitivity and specificity. Smart-phones and smart devices do have the potential to bring the monitoring and early diagnosis of complications of diabetes into the patient’s domain with the use of applications on their smart devices which will make future management of diabetes as an “e-disease management”. AI applications offer greater accuracy, efficiency, ease of use and satisfaction and can complement the management and early identifi-cation of complication of diabetes in long run.

Automated diabetic retinopathy screening for primary care settings using deep learning

Diabetic Retinopathy (DR) is one of the leading causes of blindness in the United States and other high-income countries. Early detection is key to prevention, which could be achieved effectively with a fully automated screening tool performing well on clinically relevant measures in primary care settings. We have built an artificial intelligence-based tool on a cloud-based platform for large-scale screening of DR as referable or non-referable. In this paper, we aim to validate this tool built using deep learning based techniques. The cloud-based screening model was developed and tested using deep learning techniques with 88702 images from the Kaggle dataset and externally validated using 1748 high-resolution images of the retina (or fundus images) from the Messidor-2 dataset. For validation in the primary care settings, 264 images were taken prospectively from two diabetes clinics in Queens, New York. The images were uploaded to the cloud-based software for testing the automated system as compared to expert ophthalmologists' evaluations of referable DR. Measures used were area under the curve (AUC), sensitivity, and specificity of the screening model with respect to professional graders. The screening system achieved a high sensitivity of 99.21% and a specificity of 97.59% on the Kaggle test dataset with an AUC of 0.9992. The system was also externally validated in Messidor-2, where it achieved a sensitivity of 97.63% and a specificity of 99.49% (AUC, 0.9985). On primary care data, the sensitivity was 92.3% overall (12/13 referable images are correctly identified), and overall specificity was 94.8% (233/251 non-referable images). The proposed DR screening tool achieves state-of-the-art performance among the publicly available datasets: Kaggle and Messidor-2 to the best of our knowledge. The performance on various clinically relevant measures demonstrates that the tool is suitable for screening and early diagnosis of DR in primary care settings.

Role of optical coherence tomography-angiography in diabetes mellitus: Utility in diabetic retinopathy and a comparison with fluorescein angiography in vision threatening diabetic retinopathy.

Purpose:To determine the utility of optical coherence tomography-angiography (OCT-A) in diabetic retinopathy (DR) and comparison versus fluorescein angiography (FA) in vision-threatening diabetic retinopathy (VTDR).Methods:In this cross-sectional observational study, 60 eyes with no DR (NDR), 60 eyes with non-proliferative diabetic retinopathy (NPDR), and 60 eyes with proliferative diabetic retinopathy (PDR) underwent OCT-A. FA was done in VTDR. OCT-A of the NDR eyes was analyzed by two independent retina specialists. Vessel density (VD) (mm/mm2), perfusion density (PD) (%), and foveal avascular zone (FAZ) (mm2) area was analyzed among the groups. Montage angiography with vitreoretinal interface (VRI) segmentation was done in PDR. A qualitative comparison was done between OCT-A and FA for features of DR.Results:OCT-A detected 16.66% of the eyes with microaneurysm and 57.5% of the patients with capillary non-perfusion (CNP) areas in the NDR group. The inter-grader coefficient between the two observers was 0.820 for microaneurysm and 0.880 for CNP. The mean VD in NDR, NPDR, and PDR was 16.865, 13.983, and 11.643 mm/mm2. The mean PD in NDR, NPDR, and PDR was 30.595, 26.853, and 23.193%. The VD and PD values were statistically significant (P < 0.001). The mean FAZ area was not statistically significant (NPDR and PDR) (P > 0.05). The VRI showed elevated neovascularization in four eyes. OCT-A delineated microaneurysm and FAZ in 97/97 eyes who underwent FA. The FA failed to delineate FAZ in 2/37 NPDR eyes and 13/60 PDR eyes. The CNP areas (OCT-A) were detectable in all eyes. The FA demonstrated CNP areas in 17/37 and 36/60 eyes in NPDR and PDR, respectively. The FA could show peripheral CNP.Conclusion:The OCT-A helps in the early diagnosis of DR by providing vascular indices which are consistent with disease progression. OCT-A is non-invasive and ideal for follow-up. FA is a dynamic test with a larger field of view.

Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review

Diabetic Retinopathy (DR) is an eye disease that mainly affects people with diabetes. People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage. Once the vision is lost, it cannot be regained but can be prevented from causing any further damage. Early diagnosis of DR is required for preventing vision loss, for which a trained ophthalmologist is required. The clinical practice is time-consuming and is not much successful in identifying DR at early stages. Hence, Computer-Aided Diagnosis (CAD) system is a suitable alternative for screening and grading of DR for a larger population. This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques. The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.

Detection of Microaneurysms Using Grey Wolf Optimization for Early Diagnosis of Diabetic Retinopathy

The diabetic retinopathy is the leading cause of blindness worldwide, so early detection of diabetic retinopathy is necessary to reduce eye-related diseases. The accurate identification of microaneurysms is crucial for the detection of diabetic retinopathy, because it appears as the first sign of the disease. In this study, a new model is proposed to detect microaneurysms from the retinal images for early diagnosis of diabetic retinopathy. At first, the fundus images are collected from e-ophtha microaneurysms and DiaretDB1 datasets. Next, image pre-processing is accomplished using image normalization, low light image enhancement, gradient weighting and shade correction. The pre-processing methods significantly brighten the contrast of the fundus images for better visual quality and extract the hidden details of the dark conditions. In addition, Hessian based filter and Otsu threshold are used to extract the foreground objects like microaneurysms from the enhanced fundus images. At last, Grey Wolf Optimization (GWO) is used to predict the correctness of segmented microaneurysms candidates. The experimental results have revealed that the proposed model enhanced the microaneurysms detection up to 0.06-0.30 f-score value compared to the other existing models local convergence index features and local features with k-nearest neighbor. In addition, the proposed model has achieved 85.72% and 86.16% of accuracy respectively on e-ophtha microaneurysms and DiaretDB1 datasets.

A Fuzzy clustering Approach for Micro Aneurysm Lesion Detection in Diabetic Retinopathy

Diabetic retinopathy (DR) be the significant difficulty of diabetes, and micro aneurysm (MA) is an earliest diabetic retinopathy lesion, making early detection of MA a key factor in diabetic retinopathy. DR is a direct or indirect effect on human vision caused by chronic diabetes. During its early stages DR is asymptomatic, and the late diagnosis leads to undeviating vision loss. The computer-assisted diagnosis helps with prompt and effective care, with the aid of medical photos. MA mark the beginning of DR making it a vital screening stage for this disorder. Diabetic retinopathy is a persistent infection of eye that can be the reason of blindness unless it is diagnosed and treated in due course. Early discovery with analysis of diabetic retinopathy is vital to vision preservation of patient. Precise recognition of MA be the crucial method towards early diagnosis of DR, since they occur as the first symptom of the disease. The segmentation of MA is performed using the Fuzzy C algorithm, and the extraction of features is performed with Gray Level Co-occurrence Matrix ( GLCM) as the set of characteristic for KNN. This technique aims to improve classification accuracy within an ensemble. A procedure is suggested here that recognizes the first DR sign called MA using images from the retinal fundus. Effective diagnosis of DR is very critical in the defense of patients' right to see. The procedure proposed is tested using publicly available databases of retinal images and greater accuracy is achieved.

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy.

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

Analysis of retinal nerve fiber layer birefringence in patients with glaucoma and diabetic retinopathy by polarization sensitive OCT.

The retinal nerve fiber layer (RNFL) is a fibrous tissue that shows form birefringence. This optical tissue property is related to the microstructure of the nerve fiber axons that carry electrical signals from the retina to the brain. Ocular diseases that are known to cause neurologic changes, like glaucoma or diabetic retinopathy (DR), might alter the birefringence of the RNFL, which could be used for diagnostic purposes. In this pilot study, we used a state-of-the-art polarization sensitive optical coherence tomography (PS-OCT) system with an integrated retinal tracker to analyze the RNFL birefringence in patients with glaucoma, DR, and in age-matched healthy controls. We recorded 3D PS-OCT raster scans of the optic nerve head area and high-quality averaged circumpapillary PS-OCT scans, from which RNFL thickness, retardation and birefringence were derived. The precision of birefringence measurements was 0.005°/µm. As compared to healthy controls, glaucoma patients showed a slightly reduced birefringence (0.129 vs. 0.135°/µm), although not statistically significant. The DR patients, however, showed a stronger reduction of RNFL birefringence (0.103 vs. 0.135°/µm) which was highly significant. This result might open new avenues into early diagnosis of DR and related neurologic changes.

Current Challenges and Barriers to Real-World Artificial Intelligence Adoption for the Healthcare System, Provider, and the Patient.

Artificial intelligence (AI), or the use of automated systems that display the ability to correctly interpret, to learn from, and to achieve specific goals by use of external data, is an emerging technology that has myriad implications for changing the way we interact with the world. Although this technology is already being used in many fields such as banking, retail, and education, AI has the potential to transform other fields including healthcare. Within healthcare, ophthalmology is uniquely positioned to benefit from AI not only through clinical decision support technology but also through improved image processing innovations such as real-time segmentation, automated image quality improvements, and assisted or autonomous disease screening tools.1,2 Although there are now Food and Drug Administration–approved technologies within ophthalmology, such as IDx-DR (Coralville, IA, USA) for early diagnosis of diabetic retinopathy and diabetic macular edema, numerous challenges still exist to realize the potentially transformative impact of these technologies in day to day practice. The need for the ophthalmology community to take a thoughtful approach to AI innovation and implementation is accentuated by the high stakes involved. The impact of misleading patients and clinicians on a health condition is much greater than a retail store misinterpreting the next book you may like to buy. As a result, we need to increase discussion about the issues surrounding who, what, when, how, and why we might use AI in practice, including ethical and liability considerations, to determine how best to implement AI for all stakeholders including practitioners, patients, practices/hospitals, and industry. This article aims to highlight the challenges and barriers to real-world AI adoption that impact the technology's utility. We examine these specific challenges that will face health care organizations, providers, and patients.

Small Target Recognition Using Dynamic Time Warping and Visual Attention

Abstract Microaneurysm is a kind of small targets in color retinal image, and it is an essential work to recognize the small target for the early diagnosis of diabetic retinopathy. This paper proposes an efficient method to accurately recognize microaneurysm. A symmetric extended curvature Gabor wavelet is presented to generate candidate objects, where some novel features are extracted for classification. A kind of statistic features is generated to distinguish between microaneurysm and thin vessels, in terms of the shape similarity of cross-section profiles. Furthermore, the visual attention-based features are proposed to compute local contrast of small targets in complex background. Random undersampling with AdaBoost (RUSBoost) classifier is employed to discriminate true microaneurysm from an overwhelming amount of candidate objects. Experimental results demonstrate that the proposed method achieves significant sensitivity and accuracy on the public datasets, in comparison to the state-of-the-arts.

Combining MF-DFA and LSSVM for retina images classification

Diabetic retinopathy is the main cause of blindness in adults. Early diagnosis of diabetic retinopathy is essential for avoiding deterioration of illness and vision loss. The use of computer technology to identify diabetic retinopathy images provides significant means to reduce the risk of deterioration. In this paper, we propose a new approach for retina images detection and classification by using a hybrid system which is constructed by two-dimensional multifractal detrended fluctuation analysis (2D MF-DFA) and least square support vector machines (LSSVM). In the proposed method, we applied 2D MF-DFA to compute the local generalized Hurst exponents which are the multifractal features of the diabetic retinopathy image, and these values are recorded as LHq. Then, the Hurst exponents are taken as the training input vector for the training in LSSVM. Finally, we classified a specific retina image as healthy or lesion image. We present experimental verification to investigate the efficiency and robustness of the proposed system. The results show that the proposed system yields a classification accuracy with 99.01% ± 0.0074, sensitivity with 99.03% ± 0.0051, and specificity with 97.73% ± 0.0075. When the performance was compared with state-of-the-arts, the solution indicated that the MF-DFA-LSSVM system outperforms most of others in terms of all the classification sensitivity, accuracy, and specificity. The proposed method will be useful for clinical medicine.

Detection of Diabetic Macular Edema in Optical Coherence Tomography Image Using an Improved Level Set Algorithm.

Diabetic macular edema (DME) is a major cause of visual loss in the patients with diabetic retinopathy. DME detection in Optical Coherence Tomography (OCT) image contributes to the early diagnosis of diabetic retinopathy and blindness prevention. Currently, DME detection in the OCT image mainly relies on the handwork by the experienced clinician. It is a laborious, time-consuming, and challenging work to organize a comprehensive DME screening for diabetic patients. In this study, we proposed a novel algorithm for the detection and segmentation of DME region in OCT image based on the K-means clustering algorithm and improved Selective Binary and Gaussian Filtering regularized level set (SBGFRLS) algorithm named as SBGFRLS-OCT algorithm. SBGFRLS-OCT algorithm was compared with the current level set algorithms, including C-V (Chan-Vese), GAC (geodesic active contour), and SBGFRLS, to estimate the performance of DME detection. SBGFRLS-OCT algorithm was also compared with the clinician to estimate the precision, sensitivity, and specificity of DME segmentation. Compared with C-V, GAC, and SBGFRLS algorithm, the SBGFRLS-OCT algorithm enhanced the accuracy and reduces the processing time of DME detection. Compared with manual DME segmentation, the SBGFRLS-OCT algorithm achieved a comparable precision (97.7%), sensitivity (91.8%), and specificity (99.2%). Collectively, this study presents a novel algorithm for DME detection in the OCT image, which can be used for mass diabetic retinopathy screening.

Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

NFN＋: A novel network followed network for retinal vessel segmentation

In the early diagnosis of diabetic retinopathy, the morphological attributes of blood vessels play an essential role to construct a retinal computer-aided diagnosis system. However, due to the challenges including limited densely annotated data, inter-vessel differences and structured prediction problem, it remains challenging to segment accurately the retinal vessels, particularly the capillaries on color fundus images. To address these issues, in this paper, we propose a novel deep learning-based model called NFN＋ to effectively extract multi-scale information and make full use of deep feature maps. In NFN＋, the front network converts an image patch into a probabilistic retinal vessel map, and the followed network further refines the map to achieve a better post-processing module, which helps represent the vessel structures implicitly. We employ the inter-network skip connections to unite two identical multi-scale backbones, which enables the useful multi-scale features to be directly transferred from shallow layers to deeper layers. The refined probabilistic retinal vessel maps produced from the augmented images are then averaged to construct the segmentation results. We evaluated this model on the digital retinal images for vessel extraction (DRIVE), structured analysis of the retina (STARE), and the child heart and health study (CHASE) databases. Our results indicate that the elaborated cascaded designs can produce performance gain and the proposed NFN＋ model, to our best knowledge, achieved the state-of-the-art retinal vessel segmentation accuracy on color fundus images (AUC: 98.30%, 98.75% and 98.94%, respectively).

Enforcing artificial neural network in the early detection of diabetic retinopathy OCTA images analysed by multifractal geometry

Diabetic retinopathy (DR) is one of the leading causes of vision loss. It causes neovascularization with blocking the regular small blood vessels. Early detection helps the ophthalmologist in patient treatment and prevents or delays vision loss. In this work, multifractal analysis has been used in some details to automate the diagnosis of diabetic without diabetic retinopathy and non-proliferative DR. Concerning using number of multifractal geometrical methods, as a necessary second step the enforcement of the sophisticated artificial neural network has been consultant in order to improve the accuracy of the obtained results. Patients and methods: Thirty normal cases’ eyes, 30 diabetic without DR patients’ eyes and 30 non-proliferative diabetic retinopathy (mild to moderate) eyes are exposed to optical coherence tomography angiography (OCTA) to get image superficial layer of macula for all cases. These images were approved in Ophthalmology Center in Mansoura University, Egypt, and medically were diagnosed by the ophthalmologists. We extract the most changeable features that associated to the morphological retinal vascular network alternations. The seven extracted features are related to the multifractal analysis results, which describe the vascular network architecture and gaps distribution. A supervised Artificial Neural Network (ANN) is used to classify the images into three categories: normal, diabetic without diabetic retinopathy and non-proliferative DR. Results: The human retinal blood vascular network architecture is found to be a fractal system. Multifractal geometry describes the irregularity and gaps distribution in the retina. We extracted seven features from the studied images. The features were the generalized dimensions D0 , D1 , D2 , α at the maximum f(α) singularity spectrum, the spectrum width, the spectrum symmetrical shift point and lacunarity. The ANN obtains a single value decision with classification accuracy 97.78%, with minimum sensitivity 96.67%. Conclusion: Early stages of DR could be noninvasively detected using high-resolution OCTA images that were analysed by multifractal geometry parameterization and implemented by the sophisticated artificial neural network with classification accuracy 96.67%. This approach could promote risk stratification for the decision of early diagnosis of diabetic retinopathy.

Sensitive tear screening of diabetic retinopathy with dual biomarkers enabled using a rapid electrokinetic patterning platform.

Bead-based immunosensors have intrigued the scientific community over the past decades due to their rapid and multiplexed capabilities in the detection of various biological targets. Nevertheless, their use in the detection of low-abundance analytes remains a continuing challenge because of their limited number of active enrichment approaches. To this end, our research presents a delicate microbead enrichment technique using an optoelectrokinetic platform, followed by the detection of dual biomarkers for the sensitive screening of an eye disease termed diabetic retinopathy (DR). In this study, microbeads turned fluorescent as their surfaces formed sandwiched immunocomplexes in the presence of target antigens. The tiny fluorescent dots were then concentrated using the optoelectrokinetic platform for the enhancement of their signals. The signal rapidly escalated in 10 s, and the optimal limit of detection was nearly 100 pg mL-1. For practical DR screening, two biomarkers, lipocalin 1 (LCN1) and vascular endothelial growth factor (VEGF), were used. Approximately 20 μL of analytes were collected from the tear samples of the tested patients. The concentrations of both biomarkers showed escalating trends with the severity of DR. Two concentration thresholds of LCN1 and VEGF that indicate proliferative DR were determined out of 24 clinical samples based on the receiver operating characteristic curves. For verification, a single-blind test was conducted with additional clinical tear samples from five random subjects. The final outcome of this evaluation showed an accuracy of >80%. This non-invasive screening provides a potential means for the early diagnosis of DR and may increase the screening rate among the high-risk diabetic population in the future.

An Enhanced Residual U-Net for Microaneurysms and Exudates Segmentation in Fundus Images

Diabetic retinopathy (DR) is a leading cause of visual blindness. However if DR can be diagnosed and treated early, 90% of DR causing blindness can be prevented significantly. Microaneurysms (MAs) and exudates (EXs), as signs of DR, can be used for early DR diagnosis. However, MAs and EXs segmentation is a challenging task due to the low contrast of the lesions, the interference of noises, and the imbalance between the lesion areas and the background. In this paper, an enhanced residual U-Net (ERU-Net) for MAs and EXs segmentation is proposed. ERU-Net obtains three U-paths, which are composed by three upsampling paths together with one downsampling path. With such three U-paths structure, ERU-Net can enhance the corresponding features fusion and capture more details of fundus images. Also, a residual block is constructed in ERU-Net to extract more representative features. In the experiments, we evaluate the performance of ERU-Net for MAs and EXs segmentation on three public datasets, E-Ophtha, IDRiD, and DDR. The ERU-Net obtains the AUC values of 0.9956, 0.9962, 0.9801, 0.9866, 0.9679, 0.9609 for MAs and EXs segmentation on these three datasets, respectively, which are greater than that of the original U-Net. Compared with some traditional methods, convolutional neural networks and other recent U-Nets, ERU-Net also performs competitively. Besides, we have applied ERU-Net to segment optic disc (OD) on the DRISHTI-GS1 dataset, achieving the highest Jaccard index of 0.994 compared with the existing methods. The numerical results indicate that ERU-Net is a promising network for medical image segmentation.

PET Imaging of Vesicular Monoamine Transporter 2 in Early Diabetic Retinopathy Using [18F]FP-(+)-DTBZ.

Diabetic retinopathy (DR) is characterized by dopaminergic neuron loss in the retina of the eyes. [18F]fluoropropyl-(+)-dihydrotetrabenazine ([18F]FP-(+)-DTBZ) positron emission tomography (PET) has been shown to detect dopaminergic neuron loss. The study is to investigate the feasibility of PET imaging with [18F]FP-(+)-DTBZ for early diagnosis of diabetic retinopathy (DR) in diabetes mellitus (DM) rat models. The DM rat model was established by a single intraperitoneal injection of streptozotocin (STZ) (65 mg/kg). After 4 weeks, 8 weeks, and 12 weeks of STZ injection, the retinas of the rats were evaluated by electroretinogram (ERG), color fundus photography (CFP), fundus fluorescein angiography (FFA), and small animal PET scan with [18F]FP-(+)-DTBZ by targeting vesicular monoamine transporter 2 (VMAT2). [18F]FP-(+)-DTBZ uptake in retina was quantified as standardized uptake value (SUV). Immunofluorescence staining and Western blot were also performed to confirm the expression level of VMAT2 in retina. ERG dysfunction was observed at 8 weeks in STZ-diabetic rats, evidenced by smaller amplitudes of oscillatory potentials (OPs) when compared with OPs in normal rats. CFP and FFA showed no significant difference in vascular leakage and neovascularization between STZ-diabetic retinas and normal ones until 8 weeks. PET imaging revealed that the SUV of [18F]FP-(+)-DTBZ was significantly lower in the STZ-diabetic retinas compared with the normal ones as early as of week 4. The results from immunofluorescence staining and Western blots confirmed the early findings in PET imaging studies. Early DR can be non-invasively detected with PET imaging using [18F]FP-(+)-DTBZ targeting VMAT2. The expression level of VMAT2 in retina may act as a new biomarker for early DR diagnosis.

Deep learning based early stage diabetic retinopathy detection using optical coherence tomography

Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal examinations on all diabetic patients is an unmet need, and detection at an early stage can provide better control of the disease. The objective of this study is to provide an optical coherence tomography (OCT) image based diagnostic technology for automated early DR diagnosis, including at both grades 0 and 1. This work can help ophthalmologists with evaluation and treatment, reducing the rate of vision loss, and enabling timely and accurate diagnosis. In this work, we developed and evaluated a novel deep network – OCTD_Net, for early-stage DR detection. While one of the networks extracted features from the original OCT image, the other extracted retinal layer information. The accuracy, sensitivity and specificity was 0.92, 0.90 and 0.95, respectively. Our analysis of retinal layers and the features learned by the proposed network suggests that grade 1 DR patients present with significant changes in the thickness and reflection of certain retinal layers. However, grade 0 DR patients do not have such significant changes. The heatmaps of the trained network also suggest that patients with early DR showed different textures around the myoid and ellipsoid zones, inner nuclear layers, and photoreceptor outer segments, which should all receive dedicated attention for early DR diagnosis.