Segmentation Of Microaneurysms Research Articles

Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy.

This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or hyperreflective on structural optical coherence tomography (OCT) images in patients with non-proliferative diabetic retinopathy (NPDR). A retrospective cohort of 249 patients (498 eyes) diagnosed with NPDR was analysed. Structural OCT scans were obtained using the Heidelberg Spectralis HRA + OCT device. Manual segmentation of MAs was performed by five masked readers, with an expert grader ensuring consistent labeling. Two deep learning models, YOLO (You Only Look Once) and DETR (DEtection TRansformer), were trained using the annotated OCT images. Detection and classification performance were evaluated using the area under the receiver operating characteristic (ROC) curves. The YOLO model performed poorly with an AUC of 0.35 for overall MA detection, with AUCs of 0.33 and 0.24 for hyperreflective and hyporeflective MAs, respectively. The DETR model had an AUC of 0.86 for overall MA detection, but AUCs of 0.71 and 0.84 for hyperreflective and hyporeflective MAs, respectively. Post-hoc review revealed that discrepancies between automated and manual grading were often due to the automated method's selection of normal retinal vessels. The choice of deep learning model is critical to achieving accuracy in detecting and classifying MAs in structural OCT images. An automated approach may assist clinicians in the early detection and monitoring of diabetic retinopathy, potentially improving patient outcomes.

Diabetic Retinopathy Image Lesion Segmentation with Feature Fusion Relation Transformer Network

Diabetes is a common disease that affects different vital organs of the human body, including the eyes. In diabetic patients, a change in blood sugar level leads to eye problems. Around 80% of the patients who have diabetes for more than 10 years have severe eye-related pathological disorders such as retinopathy and maculopathy. Proper detection, diagnosis, and treatment of eye-related pathologies prevent damage to the eye during the earliest stages of diabetic disease—the developed stage findings in patients losing their vision. The retinal damage due to diabetes is termed Diabetic Retinopathy (DR). The treatment of DR involves detecting the presence of the disease in the form of microaneurysms (MA), hemorrhages (HE), and exudates (EX) in the retinal area. The process of segmenting a massive segment of Retinal Images (RI) performs a prominent role in DR classification. The existing research concentrates on Optic Disc (OD) segmentation. This article focuses on the segmentation of MA, HE, and EX using a Feature Fusion Relation Transformer Network (FFRTNet). In this research, the benchmark dataset, the Indian Diabetic Retinopathy Image Dataset (IDRID), is used for the ablation study to evaluate the use of every module. The proposed method, FFRTNet, is compared with state-of-the-art methods. The evaluation of FFRTNet enhances the segmentation by 3.56%, 4.34%, and 3.75% on metrics, namely sensitivity, Intersection-over-Union (IoU), and Dice coefficient (DICE). The qualitative and quantitative results proved the superiority of FFRTNet in segmenting lesions in DR.

Ensembling U-Nets for microaneurysm segmentation in optical coherence tomography angiography in patients with diabetic retinopathy

Diabetic retinopathy is one of the leading causes of blindness around the world. This makes early diagnosis and treatment important in preventing vision loss in a large number of patients. Microaneurysms are the key hallmark of the early stage of the disease, non-proliferative diabetic retinopathy, and can be detected using OCT angiography quickly and non-invasively. Screening tools for non-proliferative diabetic retinopathy using OCT angiography thus have the potential to lead to improved outcomes in patients. We compared different configurations of ensembled U-nets to automatically segment microaneurysms from OCT angiography fundus projections. For this purpose, we created a new database to train and evaluate the U-nets, created by two expert graders in two stages of grading. We present the first U-net neural networks using ensembling for the detection of microaneurysms from OCT angiography en face images from the superficial and deep capillary plexuses in patients with non-proliferative diabetic retinopathy trained on a database labeled by two experts with repeats.

A Cascade U‐Net With Transformer for Retinal Multi‐Lesion Segmentation

ABSTRACTDiabetic retinopathy (DR) is an important cause of blindness. If not diagnosed and treated in a timely manner, it can lead to irreversible vision loss. The diagnosis of DR relies heavily on specialized ophthalmologists. In recent years, with the development of artificial intelligence a number of diagnostics using this technique have begun to appear. One method for diagnosing diseases in this field is to segment four common kinds of lesions from color fundus images, including: exudates (EX), soft exudates (SE), hemorrhages (HE), and microaneurysms (MA). In this paper, we propose a segmentation model for DR based on deep learning. The main part of the model consists of two layers of improved U‐Net network based on transformer, corresponding to the two stages of coarse segmentation and fine segmentation, respectively. The model can segment four common kinds of lesions from the input color fundus image at the same time. To validate the performance of our proposed model, we test our model on three public datasets: IDRiD, DDR, and DIARETDB1. The test results show that our proposed model achieves competitive results compared with the existing methods in terms of PR‐AUC, ROC‐AUC, Dice, and IoU, especially for lesions segmentation of SE and MA.

Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9.

Computer vision is a powerful tool in medical image analysis, supporting the early detection and classification of eye diseases. Diabetic retinopathy (DR), a severe eye disease secondary to diabetes, accompanies several early signs of eye-threatening conditions, such as microaneurysms (MAs), hemorrhages (HEMOs), and exudates (EXs), which have been widely studied and targeted as objects to be detected by computer vision models. In this work, we tested the performances of the state-of-the-art YOLOv8 and YOLOv9 architectures on DR fundus features segmentation without coding experience or a programming background. We took one hundred DR images from the public MESSIDOR database, manually labelled and prepared them for pixel segmentation, and tested the detection abilities of different model variants. We increased the diversity of the training sample by data augmentation, including tiling, flipping, and rotating the fundus images. The proposed approaches reached an acceptable mean average precision (mAP) in detecting DR lesions such as MA, HEMO, and EX, as well as a hallmark of the posterior pole of the eye, such as the optic disc. We compared our results with related works in the literature involving different neural networks. Our results are promising, but far from being ready for implementation into clinical practice. Accurate lesion detection is mandatory to ensure early and correct diagnoses. Future works will investigate lesion detection further, especially MA segmentation, with improved extraction techniques, image pre-processing, and standardized datasets.

Segmentation of retinal microaneurysms in fluorescein fundus angiography images by a novel three-step model.

Microaneurysms serve as early signs of diabetic retinopathy, and their accurate detection is critical for effective treatment. Due to their low contrast and similarity to retinal vessels, distinguishing microaneurysms from background noise and retinal vessels in fluorescein fundus angiography (FFA) images poses a significant challenge. We present a model for automatic detection of microaneurysms. FFA images were pre-processed using Top-hat transformation, Gray-stretching, and Gaussian filter techniques to eliminate noise. The candidate microaneurysms were coarsely segmented using an improved matched filter algorithm. Real microaneurysms were segmented by a morphological strategy. To evaluate the segmentation performance, our proposed model was compared against other models, including Otsu's method, Region Growing, Global Threshold, Matched Filter, Fuzzy c-means, and K-means, using both self-constructed and publicly available datasets. Performance metrics such as accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union were calculated. The proposed model outperforms other models in terms of accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union. The segmentation results obtained with our model closely align with benchmark standard. Our model demonstrates significant advantages for microaneurysm segmentation in FFA images and holds promise for clinical application in the diagnosis of diabetic retinopathy. The proposed model offers a robust and accurate approach to microaneurysm detection, outperforming existing methods and demonstrating potential for clinical application in the effective treatment of diabetic retinopathy.

Deep CNN-based microaneurysm segmentation system in retinal images using multi-level features

Microaneurysms, tiny, circular red dots that occur in retinal fundus images, are one of the earliest symptoms of diabetic retinopathy. Because microaneurysms are small and delicate, detecting them can be difficult. Their small size and cunning character make automatic detection of them difficult. In this study, a novel encoder-decoder network is proposed to segment the MAs automatically and accurately. The encoder part mainly consists of three parts: a low-level feature extraction module composed of a dense connectivity block (Dense Block), a High-resolution Block (HR Block), and an Atrous Spatial Pyramid Pooling (ASPP) module, of which the latter two modules are used to extract high-level information. Therefore, the network is named a Multi-Level Features based Deep Convolutional Neural Network (MF-DCNN). The proposed decoder takes advantage of the multi-scale features from the encoder to predict MA regions. Compared with the existing methods on three datasets, it is proved that the proposed method is better than the current excellent methods in the segmentation results of the normal and abnormal fundus. In the case of fewer network parameters, MF-DCNN achieves better prediction performance on intersection over union (IoU), dice similarity coefficient (DSC), and other evaluation metrics. MF-DCNN is lightweight and able to use multi-scale features to predict MA regions. It can be used to automatically segment the MA and assist in computer-aided diagnosis.

Novel Contraharmonic Correlative Attention Loss for Microaneurysm Segmentation in Fundus Images

Novel Contraharmonic Correlative Attention Loss for Microaneurysm Segmentation in Fundus Images

Automatic Detection of Microaneurysms in Fundus Images

Early detection and treatment of diabetic retinopathy can delay blindness and improve quality of life for diabetic patients. It is difficult to detect early symptoms of diabetic retinopathy, which is presented by few microaneurysms in fundus images. This study proposes an algorithm to detect microaneurysms in fundus images automatically. The proposal includes microaneurysms segmentation by U-Net model and their false positives removal by ResNet model. The effectiveness of the proposal is evaluated with the public database IDRiD and E-ophtha by the area under precision recall curve (AUPR). 90% of microaneurysms can be detected at early stages of diabetic retinopathy. This proposal outperforms previous methods based in AUPR evaluation.

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

PurposeAccurate 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.MethodAOSLO-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.ResultsThe 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.ConclusionsWe demonstrate that AOSLO-net provides high-quality of MA segmentation from AOSLO images that enables correct MA morphological classification.Translational RelevanceAs 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.

Segmentation of Microaneurysms for Early Detection of Diabetic Retinopathy Using MResUNet

Segmentation of Microaneurysms for Early Detection of Diabetic Retinopathy Using MResUNet

A multi-scale segmentation-to-classification network for tiny microaneurysm detection in fundus images

Microaneurysms (MAs) are recognized as the earliest indicators of Diabetic retinopathy (DR), which play an important role in DR screening. However, the detection of MAs is full of challenges due to the fact that the MAs are tiny spots in the complex situations. In this paper, we propose a multi-scale segmentation-to-classification model to improve the accuracy of d MAs detection in the complex situations. This proposed model is primarily composed of two stages: (1) the segmentation of MAs and; (2) the classification for MAs. In the segmentation stage, a multi-scale residual network, termed as MSRNet, is designed to explore the necessary information from the MAs with large size variance. In the classification stage, a multi-scale efficient network, named as MS-EfficientNet, is built to discriminate the false MAs from the MA candidates detected by the MSRNet. Furthermore, we reasonably expand the positive patches by adding constrained variances to the MA ground truths. Experiments show that the proposed model significantly outperforms the state-of-the-art with an average sensitivity score of 0.715 on the E-ophtha-MA datasets. The further results with other datasets also demonstrate that the proposed model for MAs segmentation is highly effective and robust. Our source code will be available on the GitHub: https://github.com/Lyblue11/Microaneurysm-Detection.

Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations

Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are important indicators of diabetic retinopathy progression. The authors introduce a two-stage deep learning approach for microaneurysms segmentation using multiple scales of the input with selective sampling and embedding triplet loss. The proposed approach facilitates a region proposal fully convolutional neural network trained on segmented patches and a patch-wise refinement network for improving the results suggested by the first stage hypothesis. To enhance the discriminative power of the second stage refinement network, the authors use triplet embedding loss with a selective sampling routine that dynamically assigns sampling probabilities to the oversampled class patches. This approach introduces a 23.5 % relative improvement over the vanilla fully convolutional neural network on the Indian Diabetic Retinopathy Image Data set segmentation data set. The proposed segmentation is incorporated in a classification model to solve two downstream tasks for diabetic retinopathy detection and referable diabetic retinopathy detection. The classification tasks are trained on the Kaggle diabetic retinopathy challenge data set and evaluated on the Messidor data. The authors show that adding the segmentation enhances the classification performance and achieves comparable performance to the state-of-the-art models.

Modified U-Net architecture for semantic segmentation of diabetic retinopathy images

Segmentation of lesions from fundus images is an essential prerequisite for accurate severity assessment of diabetic retinopathy. Due to variation in morphologies, number and size of lesions, the manual grading process becomes extremely challenging and time-consuming. This necessitates the need of an automatic segmentation system that can precisely define the region of interest boundaries and assist ophthalmologists in speedy diagnosis along with diabetic retinopathy severity grading. The paper presents a modified U-Net architecture based on residual network and employs periodic shuffling with sub-pixel convolution initialized to convolution nearest neighbour resize. The proposed architecture has been trained and validated for microaneurysm and hard exudate segmentation on two publicly available datasets namely IDRiD and e-ophtha. For IDRiD dataset, the network obtains 99.88% accuracy, 99.85% sensitivity, 99.95% specificity and dice score of 0.9998 for both microaneurysm and exudate segmentation. Further, when trained on e-ophtha and validated on IDRiD dataset, the network shows 99.98% accuracy, 99.88% sensitivity, 99.89% specificity and dice score of 0.9998 for microaneurysm segmentation. For exudates segmentation, the model obtained 99.98% accuracy, 99.88% sensitivity, 99.89% specificity and dice score of 0.9999, when trained on e-ophtha and validated on IDRiD dataset. In comparison to existing literature, the proposed model provides state-of-the-art results for retinal lesion segmentation.

Deep membrane systems for multitask segmentation in diabetic retinopathy

Automatic segmentation of microaneurysms (MAs), hard exudates (EXs) and optic disc (OD) are crucial to the diagnostic assessment of diabetic retinopathy (DR). However, the small sizes of MAs and EXs, as well as the large variations in the locations and shapes of MAs and EXs make these segmentation tasks challenging. To alleviate these challenges, in this paper, we propose a novel and automatic multitask segmentation method based on a new membrane system named a dynamic membrane system with hybrid structures. Three new types of rules in the new membrane system are designed to solve complex real applications in parallel. In membrane structures, efficient convolutional neural networks (CNNs) are implemented to perform pixel-wise segmentations of MAs, EXs and OD in DR. Evaluations on three public datasets demonstrate the robustness of our proposed method for correctly segmenting MAs, EXs and OD in various settings. Our experimental results outperform the state-of-the-art methods.

Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network.

Microaneurysms (MAs) play an important role in the diagnosis of clinical diabetic retinopathy at the early stage. Annotation of MAs manually by experts is laborious and so it is essential to develop automatic segmentation methods. Automatic MA segmentation remains a challenging task mainly due to the low local contrast of the image and the small size of MAs. A deep learning-based method called U-Net has become one of the most popular methods for the medical image segmentation task. We propose an architecture for U-Net, named deep recurrent U-Net (DRU-Net), obtained by combining the deep residual model and recurrent convolutional operations into U-Net. In the MA segmentation task, DRU-Net can accumulate effective features much better than the typical U-Net. The proposed method is evaluated on two publicly available datasets: E-Ophtha and IDRiD. Our results show that the proposed DRU-Net achieves the best performance with 0.9999 accuracy value and 0.9943 area under curve (AUC) value on the E-Ophtha dataset. And on the IDRiD dataset, it has achieved 0.987 AUC value (to our knowledge, this is the first result of segmenting MAs on this dataset). Compared with other methods, such as U-Net, FCNN, and ResU-Net, our architecture (DRU-Net) achieves state-of-the-art performance.

Detection of microaneurysms using ant colony algorithm in the early diagnosis of diabetic retinopathy

Microaneurysms are lesions in the shape of small circular dilations which result from thinning in peripheral retinal blood vessels due to diabetes and increasing intra-retinal blood pressure. Because it is considered as the most important clinical finding in the diagnosis of diabetic retinopathy, accurate detection of these lesions bear utmost importance in the early diagnosis of diabetic retinopathy. The present study aims to accurately, effectively and automatically detect microaneurysms which are difficult to detect in color fundus images in early stage. To this aim, ant colony algorithm, which is an important optimization method, was used instead of conventional image processing techniques. First, retinal vascular structure was extracted from color fundus images in Messidor and DiaretDB1 data sets. Afterwards, the segmentation of microaneurysms was effectively carried out using ant colony algorithm. The same procedure was also applied to five different image processing and clustering algorithms (watershed, random walker, k-means, maximum entropy and region growing) in order to compare the performance of the proposed method with other methods. Microaneurysm images manually detected by a specialist eye doctor were used to measure the performances of above-mentioned methods. The similarities among microaneurysms which were automatically and manually segmented were tested using Dice and Jaccard similarity index values. Dice index values obtained from the study vary between 0.52 and 0.98 in maximum entropy, 0.55 and 0.88 in watershed, 0.75 and 0.86 in region growing, 0.55 and 0.78 in k-means, and 0.66 and 0.83 in random walker, and 0.81 and 0.9 in ant colony. Similar performance values were also obtained in Jaccard index. The results show that different performances were observed in the conventional segmentation of microaneurysms depending on the image quality. On the other hand, the ant colony based method proposed in this paper displays a more stabilized and higher performance irrespective of image contrast. Therefore, it is evident that the proposed method successfully detects microaneurysms even in low quality images, thus helping specialists diagnose them in an easier way.

Automated quantitative characterisation of retinal vascular leakage and microaneurysms in ultra-widefield fluorescein angiography

Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathology in the retinal vasculature such as microaneurysms (MAs) and vascular leakage. Despite its potential value for diagnosis...

An Image-Processing Strategy for the Segmentation and Quantification of Microaneurysms in Fluorescein Angiograms of the Ocular Fundus

Digital image-processing techniques can provide an objective and highly repeatable way of quantifying retinal pathology. This study describes an image-processing strategy which detects and quantifies microaneurysms present in digitized fluorescein angiograms. After preprocessing stages, a bilinear top-hat transformation and matched filtering are employed to provide an initial segmentation of the images. Thresholding this processed image results in a binary image containing candidate microaneurysms. A novel region-growing algorithm fully delineates each marked object and subsequent analysis of the size, shape, and energy characteristics of each candidate results in the final segmentation of microaneurysms. The technique is assessed by comparing the computer's results with microaneurysm counts carried out by five clinicians, using Receiver Operating Characteristic (ROC) curves. The performance of the automated technique matched that of the clinicians' analyses. This strategy is valuable in providing a way of accurately monitoring the progression of diabetic retinopathy.