Confocal MultiColor Signal Depends on Perfusion Characteristics of Retinal Microaneurysms in Diabetic Retinopathy as Detected by OCTA.

Purpose: To propose a noninvasive way of classifying multimodal imaging of retinal microaneurysms (MA) secondary to diabetic retinopathy (DR). Methods: The research was designed as a cross-sectional, observational study of patients affected by DR. Multimodal imaging included confocal MultiColor imaging, optical coherence tomography (OCT) and OCT angiography (OCTA). MA green- and infrared-reflectance components were assessed by confocal MultiColor imaging, reflectivity properties by OCT, and MA perfusion features by OCTA. In addition, we included high-resolution (HR) and high-speed (HS) OCTA scans to assess HR-HS agreement in detecting retinal MA and to highlight different perfusion features detected by both OCTA acquisitions. Results: We analyzed 216 retinal MAs, divided into green (46; 21%), red (58; 27%) and mixed types (112; 52%). Green MAs were mainly hyper-reflective on OCT, with no or poor filling on OCTA. Red MAs were characterized by an isoreflective signal on OCT and full filling on OCTA. Mixed MAs showed a hyper-reflective border and a hyporeflective core on OCT and partial filling on OCTA. No differences in red MA HR/HS size discrepancy and reflectivity were found, whereas these progressively increased as the MA MultiColor signal changed from infrared to green. MA types significantly correlated with visual acuity, DR duration, and DR severity. Conclusions: Retinal MA can be classified reliably by means of a fully noninvasive multimodal imaging-based assessment. MA types are matched with visual acuity, DR duration and DR severity. Both HR and HS OCTA are highly effective in detecting MA, although HR OCTA is to be preferred in the presence of fibrotic evolution. Translational Relevance: This study outlines a proposed novel MA classification based on noninvasive multimodal imaging. The findings presented in this paper endorse the clinical relevance of this approach, highlighting how this classification is associated with both DR duration and severity.

Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method.

In this study, a novel method for automatic microaneurysm detection in color fundus images is presented. The proposed method is based on three main steps: (1) image breakdown to smaller image patches, (2) inference to segmentation models, and (3) reconstruction of the predicted segmentation map from output patches. The proposed segmentation method is based on an ensemble of three individual deep networks, such as U-Net, ResNet34-UNet and UNet++. The performance evaluation is based on the calculation of the Dice score and IoU values. The ensemble-based model achieved higher Dice score (0.95) and IoU (0.91) values compared to other network architectures. The proposed ensemble-based model demonstrates the high practical application potential for detection of early-stage diabetic retinopathy in color fundus images.

Navigated direct photocoagulation with a 30-ms short-pulse laser for treating microaneurysms in diabetic macular edema exhibits a high closure rate.

This study carried out direct photocoagulation for treating microaneurysms (MAs) in diabetic macular edema (DME) using a navigation laser system with a 30-ms pulse duration. The MA closure rate after 3 months was investigated using pre and postoperative fluorescein angiography images. MAs primarily inside the edematous area based on optical coherence tomography (OCT) maps were selected for treatment, and leaking MAs (n = 1151) were analyzed in 11 eyes (eight patients). The total MA closure rate was 90.1% (1034/1151), and the mean MA closure rate in each eye was 86.5 ± 8.4%. Mean central retinal thickness (CRT) decreased from 471.9 ± 73.0 µm to 420.0 ± 87.5 µm (P = 0.049), and there was a correlation between the MA closure rate and the CRT reduction rate (r = 0.63, P = 0.037). There was no difference in the MA closure rate depending on the degree of edema thickness based on a false-color topographic OCT map image. Direct photocoagulation for DME with a short pulse using the navigated photocoagulator resulted in a high MA closure rate in just 3 months and a corresponding improvement in retinal thickness. These findings encourage the use of a new therapeutic approach for DME.

Evaluation of microaneurysms as predictors of therapeutic response to anti-VEGF therapy in patients with DME.

Administration of intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy is the first-line therapy for diabetic macular oedema (DME). However, some patients show no or insufficient response to repeated anti-VEGF injections. Therefore, it is necessary to identify factors that can predict this resistance against anti-VEGF treatment. Presence of microaneurysms (MAs) is a predictor of the development and progression of DME, but its relationship with the treatment response to the anti-VEGF agents is not well known. Therefore, we aimed to elucidate the relationship between the distribution of MAs and the response to anti-VEGF therapy in patients with DME. The number of MAs was measured before anti-VEGF therapy in each region using fluorescein angiography, indocyanine green angiography (IA), and optical coherence tomography angiography. Patients with DME were divided into the responder and non-responder groups after three loading phases. Differences in the distribution of MAs between the groups were investigated. Pre-treatment IA revealed more MAs in the nasal area in the non-responder group than in the responder group (10.7 ± 10.7 and 5.7 ± 5.7, respectively, in the nasal macula) (1.4 ± 2.1 and 0.4 ± 0.7, respectively, in the nasal fovea). Whereas, pre-treatment FA and OCTA could not reveal significantly difference between the groups. Detection of MAs in the nasal macula using pre-treatment IA may indicate resistance to anti-VEGF therapy. We recommend the clinicians confirm the presence of MAs in the nasal macula, as shown by IA, as a predictor of therapeutic response to anti-VEGF therapy in patients with treatment naive DME.

Automatic detection of microaneurysms in optical coherence tomography images of retina using convolutional neural networks and transfer learning.

Microaneurysms (MAs) are pathognomonic signs that help clinicians to detect diabetic retinopathy (DR) in the early stages. Automatic detection of MA in retinal images is an active area of research due to its application in screening processes for DR which is one of the main reasons of blindness amongst the working-age population. The focus of these works is on the automatic detection of MAs in en face retinal images like fundus color and Fluorescein Angiography (FA). On the other hand, detection of MAs from Optical Coherence Tomography (OCT) images has 2 main advantages: first, OCT is a non-invasive imaging technique that does not require injection, therefore is safer. Secondly, because of the proven application of OCT in detection of Age-Related Macular Degeneration, Diabetic Macular Edema, and normal cases, thanks to detecting MAs in OCT, extensive information is obtained by using this imaging technique. In this research, the concentration is on the diagnosis of MAs using deep learning in the OCT images which represent in-depth structure of retinal layers. To this end, OCT B-scans should be divided into strips and MA patterns should be searched in the resulted strips. Since we need a dataset comprising OCT image strips with suitable labels and such large labelled datasets are not yet available, we have created it. For this purpose, an exact registration method is utilized to align OCT images with FA photographs. Then, with the help of corresponding FA images, OCT image strips are created from OCT B-scans in four labels, namely MA, normal, abnormal, and vessel. Once the dataset of image strips is prepared, a stacked generalization (stacking) ensemble of four fine-tuned, pre-trained convolutional neural networks is trained to classify the strips of OCT images into the mentioned classes. FA images are used once to create OCT strips for training process and they are no longer needed for subsequent steps. Once the stacking ensemble model is obtained, it will be used to classify the OCT strips in the test process. The results demonstrate that the proposed framework classifies overall OCT image strips and OCT strips containing MAs with accuracy scores of 0.982 and 0.987, respectively.

[Detection of microaneurysms in fundus images based on improved YOLOv4 with SENet embedded].

Microaneurysm is the initial symptom of diabetic retinopathy. Eliminating this lesion can effectively prevent diabetic retinopathy in the early stage. However, due to the complex retinal structure and the different brightness and contrast of fundus image because of different factors such as patients, environment and acquisition equipment, the existing detection algorithms are difficult to achieve the accurate detection and location of the lesion. Therefore, an improved detection algorithm of you only look once (YOLO) v4 with Squeeze-and-Excitation networks (SENet) embedded was proposed. Firstly, an improved and fast fuzzy c-means clustering algorithm was used to optimize the anchor parameters of the target samples to improve the matching degree between the anchors and the feature graphs; Then, the SENet attention module was embedded in the backbone network to enhance the key information of the image and suppress the background information of the image, so as to improve the confidence of microaneurysms; In addition, an spatial pyramid pooling was added to the network neck to enhance the acceptance domain of the output characteristics of the backbone network, so as to help separate important context information; Finally, the model was verified on the Kaggle diabetic retinopathy dataset and compared with other methods. The experimental results showed that compared with other YOLOv4 network models with various structures, the improved YOLOv4 network model could significantly improve the automatic detection results such as F-score which increased by 12.68%; Compared with other network models and methods, the automatic detection accuracy of the improved YOLOv4 network model with SENet embedded was obviously better, and accurate positioning could be realized. Therefore, the proposed YOLOv4 algorithm with SENet embedded has better performance, and can accurately and effectively detect and locate microaneurysms in fundus images.

AOSLO-net: A Deep Learning-Based Method for Automatic Segmentation of Retinal Microaneurysms From Adaptive Optics Scanning Laser Ophthalmoscopy Images.

Purpose: Accurate segmentation of microaneurysms (MAs) from adaptive optics scanning laser ophthalmoscopy (AOSLO) images is crucial for identifying MA morphologies and assessing the hemodynamics inside the MAs. Herein, we introduce AOSLO-net to perform automatic MA segmentation from AOSLO images of diabetic retinas. Method: AOSLO-net is composed of a deep neural network based on UNet with a pretrained EfficientNet as the encoder. We have designed customized preprocessing and postprocessing policies for AOSLO images, including generation of multichannel images, de-noising, contrast enhancement, ensemble and union of model predictions, to optimize the MA segmentation. AOSLO-net is trained and tested using 87 MAs imaged from 28 eyes of 20 subjects with varying severity of diabetic retinopathy (DR), which is the largest available AOSLO dataset for MA detection. To avoid the overfitting in the model training process, we augment the training data by flipping, rotating, scaling the original image to increase the diversity of data available for model training. Results: The validity of the model is demonstrated by the good agreement between the predictions of AOSLO-net and the MA masks generated by ophthalmologists and skillful trainees on 87 patient-specific MA images. Our results show that AOSLO-net outperforms the state-of-the-art segmentation model (nnUNet) both in accuracy (e.g., intersection over union and Dice scores), as well as computational cost. Conclusions: We demonstrate that AOSLO-net provides high-quality of MA segmentation from AOSLO images that enables correct MA morphological classification. Translational Relevance: As the first attempt to automatically segment retinal MAs from AOSLO images, AOSLO-net could facilitate the pathological study of DR and help ophthalmologists make disease prognoses.

Hybrid multi-kernel SVM algorithm for detection of microaneurysm in color fundus images.

Diabetic retinopathy (DR) is a chronic disease that may cause vision loss in diabetic patients. Microaneurysms which are characterized by small red spots on the retina due to fluid or blood leakage from the weak capillary wall often occur during the early stage of DR, making screening at this stage is essential. In this paper, an automatic screening system for early detection of DR in retinal images is developed using a combined shape and texture features. Due to minimum number of hand-crafted features, the computational burden is much reduced. The proposed hybrid multi-kernel support vector machine classifier is constructed by learning a kernel model formed as a combination of the base kernels. This approach outperforms the recent deep learning techniques in terms of the evaluation metrics. The efficiency of the proposed scheme is experimentally validated on three public datasets - Retinopathy Online Challenge, DIARETdB1, MESSIDOR, and AGAR300 (developed for this study). Studies reveal that the proposed model produced the best results of 0.503 in ROC dataset, 0.481 in DIARETdB1, and 0.464 in the MESSIDOR dataset in terms of FROC score. The AGAR300 database outperforms the existing MA detection algorithm in terms of FROC, AUC, F1 score, precision, sensitivity, and specificity which guarantees the robustness of this system.

A new detection model of microaneurysms based on improved FC-DenseNet.

Diabetic retinopathy (DR) is a frequent vascular complication of diabetes mellitus and remains a leading cause of vision loss worldwide. Microaneurysm (MA) is usually the first symptom of DR that leads to blood leakage in the retina. Periodic detection of MAs will facilitate early detection of DR and reduction of vision injury. In this study, we proposed a novel model for the detection of MAs in fluorescein fundus angiography (FFA) images based on the improved FC-DenseNet, MAs-FC-DenseNet. FFA images were pre-processed by the Histogram Stretching and Gaussian Filtering algorithm to improve the quality of FFA images. Then, MA regions were detected by the improved FC-DenseNet. MAs-FC-DenseNet was compared against other FC-DenseNet models (FC-DenseNet56 and FC-DenseNet67) or the end-to-end models (DeeplabV3+ and PSPNet) to evaluate the detection performance of MAs. The result suggested that MAs-FC-DenseNet had higher values of evaluation metrics than other models, including pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean intersection over union (MIoU). Moreover, MA detection performance for MAs-FC-DenseNet was very close to the ground truth. Taken together, MAs-FC-DenseNet is a reliable model for rapid and accurate detection of MAs, which would be used for mass screening of DR patients.

Deep Learning Approach for Automatic Microaneurysms Detection.

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely "E-Ophtha" and "DIARETDB1", and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.

Computational investigation of blood cell transport in retinal microaneurysms.

Microaneurysms (MAs) are one of the earliest clinically visible signs of diabetic retinopathy (DR). MA leakage or rupture may precipitate local pathology in the surrounding neural retina that impacts visual function. Thrombosis in MAs may affect their turnover time, an indicator associated with visual and anatomic outcomes in the diabetic eyes. In this work, we perform computational modeling of blood flow in microchannels containing various MAs to investigate the pathologies of MAs in DR. The particle-based model employed in this study can explicitly represent red blood cells (RBCs) and platelets as well as their interaction in the blood flow, a process that is very difficult to observe in vivo. Our simulations illustrate that while the main blood flow from the parent vessels can perfuse the entire lumen of MAs with small body-to-neck ratio (BNR), it can only perfuse part of the lumen in MAs with large BNR, particularly at a low hematocrit level, leading to possible hypoxic conditions inside MAs. We also quantify the impacts of the size of MAs, blood flow velocity, hematocrit and RBC stiffness and adhesion on the likelihood of platelets entering MAs as well as their residence time inside, two factors that are thought to be associated with thrombus formation in MAs. Our results show that enlarged MA size, increased blood velocity and hematocrit in the parent vessel of MAs as well as the RBC-RBC adhesion promote the migration of platelets into MAs and also prolong their residence time, thereby increasing the propensity of thrombosis within MAs. Overall, our work suggests that computational simulations using particle-based models can help to understand the microvascular pathology pertaining to MAs in DR and provide insights to stimulate and steer new experimental and computational studies in this area.

Fluorescein Leakage and Optical Coherence Tomography Angiography Features of Microaneurysms in Diabetic Retinopathy.

RESULTS: Thirty-six, fifty-two, and seventy-nine MAs showed no, mild, and severe leakage on FA, respectively. Most MAs (61.7%) were centered in the inner nuclear layer. Cystoid spaces were observed adjacent to 60 (35.9%) MAs. MAs with severe leakage had a statistically higher flow proportion compared to MAs with no or mild leakage (both P < 0.001). Only 112 MAs (67.1%) were visualized in the OCTA en face images, while 165 MAs (98.8%) could be visualized in the OCT en face images. The location of MAs did not associate significantly with FA leakage status. The presence of nearby cystoid spaces and higher flow proportion by OCT B-scan with flow overlay correlated significantly with FA leakage status. CONCLUSION: The flow proportion of MAs observed on OCT B-scans with flow overlay might be a potential biomarker to identify leaking MAs. A combination of OCT B-scan, OCT en face, and OCTA en face images increased the detection rate of diabetic MAs in a noninvasive way.

A Magnified Adaptive Feature Pyramid Network for automatic microaneurysms detection.

Diabetic retinopathy (DR), as an important complication of diabetes, is the primary cause of blindness in adults. Automatic DR detection poses a challenge which is crucial for early DR screening. Currently, the vast majority of DR is diagnosed through fundus images, where the microaneurysm (MA) has been widely used as the most distinguishable marker. Research works on automatic DR detection have traditionally utilized manually designed operators, while a few recent researchers have explored deep learning techniques for this topic. But due to issues such as the extremely small size of microaneurysms, low resolution of fundus pictures, and insufficient imaging depth, the DR detection problem is quite challenging and remains unsolved. To address these issues, this research proposes a new deep learning model (Magnified Adaptive Feature Pyramid Network, MAFP-Net) for DR detection, which conducts super-resolution on low quality fundus images and integrates an improved feature pyramid structure while utilizing a standard two-stage detection network as the backbone. Our proposed detection model needs no pre-segmented patches to train the CNN network. When tested on the E-ophtha-MA dataset, the sensitivity value of our method reached as high as 83.5% at false positives per image (FPI) of 8 and the F1 value achieved 0.676, exceeding all those of the state-of-the-art algorithms as well as the human performance of experienced physicians. Similar results were achieved on another public dataset of IDRiD.

[Microaneurysms as a biomarker of diabetic retinopathy]. / Mikroanevrizmy kak marker diabeticheskoi retinopatii.

Diabetic retinopathy is a microvascular pathology, which is the most common complication of diabetes mellitus. Improvement of instrumental diagnostics of retinal pathologies has contributed to identification of various phenotypes of the progression of ocular fundus pathology in diabetes based on specific changes in the retina - biomarkers. In particular, microaneurysms initially described in diabetes, which are a manifestation of a wide range of systemic pathologies and retinal diseases, are an indicator of the severity of diabetic retinopathy. Dynamic changes in the number of microaneurysms are a confirmed prognostic biomarker of clinically significant macular edema. In diabetic retinopathy, microaneurysms are one of the earliest recognizable signs, and the dynamic of their formation and disappearance may serve as a predictor for the disease progression. This literature review presents the characteristics of microaneurysms based on various imaging techniques, and analyses the link between structural features and dynamic changes in microaneurysms, and progression of diabetic retinopathy.

Comparison of multicolor scanning laser ophthalmoscopy and optical coherence tomography angiography for detection of microaneurysms in diabetic retinopathy.

This study aimed to evaluate the usefulness of multicolor (MC) scanning laser ophthalmoscopy (MC-SLO) in detecting microaneurysm (MA) in eyes with diabetic retinopathy (DR). This was a retrospective cross-sectional study. Eyes with DR underwent fluorescein angiography (FA), MC-SLO, optical coherence tomography angiography (OCTA), and color fundus photography (CFP) were analyzed. The foveal region was cut in an 6 × 6 mm image and the number of MA in each image was counted by retina specialists to determine the sensitivity and positive predictive value. FA results were used as the ground standard. MAs were classified as those with early, late, or no dye leakage based on FA images. Fifty-four eyes of 35 patients with an average age of 64.5 ± 1.24 years were included. The sensitivity of MA detection was 37.3%, 15.3%, and 4.12% in MC-SLO, OCTA, and CFP, respectively (P < 0.01 in each pair).The positive predictive value was 66.4%, 46.4%, and 27.6% in MC, OCTA, and CFP, respectively (P < 0.01 in each pair). Sensitivity for MAs with early leakage was 36.4% in MC-SLO, which was significantly higher than 4.02% in OCTA. MC-SLO was more useful in detecting MA in eyes with DR than OCTA.

Associations Between Capillary Diameter, Capillary Density, and Microaneurysms in Diabetic Retinopathy: A High-Resolution Confocal Microscopy Study.

Purpose: To use high-resolution histology to define the associations between microaneurysms, capillary diameter and capillary density alterations in diabetic retinopathy (DR). Methods: Quantitative comparisons of microaneurysm number, capillary density and capillary diameter were performed between eight human donor eyes with nonproliferative DR and six age- and eccentricity-matched normal donor eyes after retinal vascular perfusion labelling. The parafovea, 3-mm, 6-mm, and 9-mm retinal eccentricities were analyzed and associations between microvascular alterations defined. Results: Mean capillary density was reduced in all retina regions in the DR group (P = 0.013). Microaneurysms occurred in all retina regions in the DR group, but the association between decreased capillary density and microaneurysm number was only significant in the 3-mm (P = 0.040) and 6-mm (P = 0.007) eccentricities. The mean capillary diameter of the DR group (8.9 ± 0.53 µm) was greater than the control group (7.60 ± 0.40 µm; P = 0.033). There was no association between capillary diameter increase and capillary density decrease (P = 0.257) and capillary diameter increase and microaneurysm number (P = 0.147) in the DR group. Within the parafovea of the DR group, capillary density was significantly reduced, and capillary diameter was significantly increased in the deep capillary plexus compared with the superficial and intermediate plexuses (all P < 0.05). Conclusions: In DR, capillary density reduction occurs across multiple retina eccentricities with a predilection for the deep capillary plexus. The association between microaneurysm number and capillary density is specific to retina eccentricity. Capillary diameter increase may be an early biomarker of DR. These findings may refine the application of optical coherence tomography angiography techniques for the management of DR.

Automated Microaneurysm Counts on Ultrawide Field Color and Fluorescein Angiography Images.

BACKGROUND: The severity and extent of microaneurysms (MAs) have been used to determine diabetic retinopathy (DR) severity and estimate the risk of DR progression over time. The recent introduction of ultrawide field (UWF) imaging has allowed ophthalmologists to readily image nearly the entire retina. Manual counting of MAs, especially on UWF images, is laborious and time-consuming, limiting its potential use in clinical settings. Automated MA counting techniques are potentially more accurate and reproducible compared to manual methods. METHOD: Review of available literature on current techniques of automated MA counting techniques on both ultrawide field (UWF) color images (CI) and fluorescein angiography (FA) images. RESULTS: Automated MA counting techniques on UWF images are still in the early phases of development with UWF-FA counts being further along. Early studies have demonstrated that these techniques are accurate and reproducible. CONCLUSION: Automated techniques may be an appropriate option for detecting and quantifying MAs on UWF images, especially in eyes with earlier DR severity. Larger studies are needed to appropriately validate these techniques and determine if they add substantially to clinical practice compared to standard DR grading.

Different retinopathy phenotypes in type 2 diabetes predict retinopathy progression.

PURPOSE: To characterize the progression in retinopathy severity of different phenotypes of mild nonproliferative diabetic retinopathy (NPDR) in patients with type 2 diabetes. DESIGN AND METHODS: Patients with type 2 diabetes and mild NPDR (ETDRS 20 or 35) were followed in a 5-year longitudinal study. Examinations, including color fundus photography (CFP) and optical coherence tomography (OCT and OCTA), were performed at baseline, 6 months and then annually. Phenotype classification was performed based on microaneurysm turnover (MAT, on CFP) and central retinal thickness (CRT, on OCT). Phenotype A is characterized by low MAT (< 6) and normal CRT; Phenotype B by low MAT (< 6) and increased CRT; and Phenotype C by higher MAT (≥ 6) with or without increased CRT. ETDRS grading of seven fields CFP was performed at the initial and last visits. RESULTS: Analysis of ETDRS grade step changes showed significant differences in diabetic retinopathy (DR) progression between the different phenotypes (p < 0.001). Of the 66 participants with phenotype A only 2 eyes (3%) presented 2-or-more-step worsening. None of the 50 participants characterized as phenotype B developed 2-step worsening, whereas 13 eyes (23.2%) characterized as phenotype C had 2-or-more-steps worsening. Phenotype C presents the higher risk for 2-or-more step worsening (OR: 15.94 95% CI: 3.45-73.71; p < 0.001) and higher sensitivity, correctly identifying 86.7% of cases at risk (AUC: 0.84 95% CI: 0.72-0.96; p < 0.001). Diabetic retinopathy severity progression was associated with HbA1c (p = 0.019), LDL levels (p = 0.043), and ocular factors as MAT (p = 0.010), MA formation rate (p = 0.014) and MA disappearance rate (p = 0.005). Capillary closure at 5-year follow-up, identified by lower vessel density (VD) on OCTA, was also associated with diabetic DR severity progression (p = 0.035). CONCLUSIONS: Different DR phenotypes in type 2 diabetes show different risks of retinopathy progression. Phenotype C is associated with increased HbA1c values and presents a higher risk of a 2-or-more-step worsening of the ETDRS severity score.

MultiColor imaging to detect different subtypes of retinal microaneurysms in diabetic retinopathy.

BACKGROUND: Retinal microaneurysms (MAs) are among the earliest signs of diabetic retinopathy (DR) and are typically detected by fluorescein angiography (FA). Confocal MultiColor is a noninvasive-imaging technique able to analyze different retinal features by capturing three simultaneous reflectance images. The main aim of the present study was to characterize morphological features of MAs by means of MultiColor images and to compare these with spectral domain optical coherence tomography (SD-OCT) and FA findings. METHODS: A cross-sectional, observational study setting was chosen. Multimodal imaging included MultiColor, SD-OCT and FA images. We performed a qualitative analysis in order to assess the relationship between MultiColor and its green- and red-reflectance components, SD-OCT (hyperreflective, hyporeflective and mixed reflectivity) and FA findings. MAs detected on our MultiColor images were then categorized in accordance with a previously published histological classification. RESULTS: In our study FA images were used to detect 153 MAs in 30 eyes displaying DR. MultiColor was able to distinguish 122 MAs (80%). We identified green (16%), red (19%), and mixed (65%) MAs, corresponding to different reflectivity features detected by SD-OCT. MAs not visualized on MultiColor images corresponded to tiny hyperreflective lesions on SD-OCT. We compared our imaging findings with a histological MA classification reported in the literature. Our findings showed a strict relationship between MA subtypes and SD-OCT, suggesting that the composition of MAs (cells + endothelium + fibrosis) may influence the signal detected in MultiColor images. CONCLUSIONS: MultiColor appears to be a useful technique for investigating MA features in patients with DR.