A Data-Aware Deep Supervised Method for Retinal Vessel Segmentation

Accurate segmentation of retinal vessels is critical to the mechanism, diagnosis, and treatment of many ocular pathologies. Due to the poor contrast and inhomogeneous background of fundus imaging and the complex structure of retinal fundus images, this makes accurate segmentation of blood vessels from retinal images still challenging. In this paper, we propose an effective framework for retinal vascular segmentation, which is innovative mainly in the retinal image pre-processing stage and segmentation stage. First, we perform image enhancement on three publicly available fundus datasets based on the multiscale retinex with color restoration (MSRCR) method, which effectively suppresses noise and highlights the vessel structure creating a good basis for the segmentation phase. The processed fundus images are then fed into an effective Reverse Fusion Attention Residual Network (RFARN) for training to achieve more accurate retinal vessel segmentation. In the RFARN, we use Reverse Channel Attention Module (RCAM) and Reverse Spatial Attention Module (RSAM) to highlight the shallow details of the channel and spatial dimensions. And RCAM and RSAM are used to achieve effective fusion of deep local features with shallow global features to ensure the continuity and integrity of the segmented vessels. In the experimental results for the DRIVE, STARE and CHASE datasets, the evaluation metrics were 0.9712, 0.9822 and 0.9780 for accuracy (Acc), 0.8788, 0.8874 and 0.8352 for sensitivity (Se), 0.9803, 0.9891 and 0.9890 for specificity (Sp), area under the ROC curve(AUC) was 0.9910, 0.9952 and 0.9904, and the F1-Score was 0.8453, 0.8707 and 0.8185. In comparison with existing retinal image segmentation methods, e.g. UNet, R2UNet, DUNet, HAnet, Sine-Net, FANet, etc., our method in three fundus datasets achieved better vessel segmentation performance and results.

Accurate retinal vessel segmentation plays a vital role in the early detection and diagnosis of ophthalmologic dis- eases such as diabetic retinopathy, glaucoma, and hypertension. Traditional image processing and machine learning techniques often struggle to capture the fine vessel structures and complex variations present in retinal fundus images. To overcome these limitations, this study proposes a hybrid deep learning framework that combines Convolutional Neural Networks (CNNs) and Gen- erative Adversarial Networks (GANs) for precise retinal vessel segmentation. In the proposed method, the CNN component serves as the feature extractor, learning multi-scale spatial and contextual features from retinal images, while the GAN framework enhances segmentation accuracy by employing a discriminator network that distinguishes between real and generated vessel maps. This adversarial learning setup encourages the generator to produce vessel segmentations that closely resemble ground truth images. Additionally, preprocessing techniques such as contrast enhancement and noise reduction are applied to improve the visibility of retinal structures, and post-processing steps refine the vessel boundaries for higher accuracy. Experimental results on publicly available retinal image datasets, such as DRIVE and STARE, demonstrate that the pro- posed GAN-CNN model outperforms conventional CNN-based segmentation methods in terms of accuracy, sensitivity, specificity, and Dice coefficient. The system achieves robust performance even under varying illumination and noise conditions. These findings indicate that integrating adversarial learning with convolutional feature extraction can significantly enhance retinal vessel segmentation, paving the way for more efficient and automated retinal disease screening systems

Purpose Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

The analysis of retinal vessel images plays a crucial role in the early diagnosis of various diseases. Existing retinal vascular segmentation algorithms rely only on convolutional neural networks (CNN) to carry out feature extraction. However, due to the local nature of the convolutional operation, the CNN-based algorithms are difficult to extract the global features of the picture and are insensitive to the position information of each feature, which leads to poor connectivity among the segmented retinal vessels and insufficient segmentation of fine vessels. Therefore, we propose a retinal vessel segmentation algorithm based on an axial transformer and CNN. In this algorithm, the axial transformer is used to encode the position of each feature of the image and extract their global features, and the CNN is used to extract the local features of the image. The experimental results on the CHASE DB1 dataset indicate that the proposed algorithm can effectively segment the fine vessels in retinal images and significantly improve the segmentation accuracy of retinal vessels.

Accurate retinal vessel segmentation from Optical Coherence Tomography Angiography (OCTA) images is vital in ophthalmic medicine, particularly for the early diagnosis and monitoring of diseases, such as diabetic retinopathy and hypertensive retinopathy. The retinal vascular system exhibits complex characteristics, including branching, crossing, and continuity, which are crucial for precise segmentation and subsequent medical analysis. However, traditional pixel-wise vessel segmentation methods focus on learning how to effectively divide each pixel into different categories, relying mainly on local features, such as intensity and texture, and often neglecting the intrinsic structural properties of vessels. This can cause suboptimal segmentation accuracy and robustness, particularly when handling low-contrast, noisy, or pathologicalimages. This study aims to integrate structural priors into a segmentation framework. Prior embeddings are used to guide the segmentation process, which encode the typical morphology and topological structure of blood vessels. Incorporating these embeddings can improve the accuracy of retinal vessel segmentation, particularly in challenging areas such as small vessels and regions with ambiguous boundaries. This approach could help to preserve the integrity and continuity of the vascular structure, resulting in more reliable and precisesegmentation. This study adopts a generative image segmentation framework. A structured representation in a latent embedding space is presented to explore retinal vessel priors. On this basis, a prior-driven retinal vessel segmentation network is introduced. First, the retinal vessel priors from ground truth data are learned, which are encoded as embedding tokens through a residual quantization reconstruction network. The learned priors are stored in a codebook. In our network, a raw OCTA image is transformed into semantic features using an encoder. Each semantic feature is subsequently represented by a set of embedding tokens from the codebook. Finally, the retinal vessels are reconstructed, preserving the integrity and continuity of the vascular structures using the learned structuralpriors. The performance of our proposed network was assessed across three OCTA datasets: publically available ROSE-1 and ROSE-2, and the private dataset OCTA-Z. Both quantitative and qualitative evaluations revealed that our network outperformed current state-of-the-art methods. In particular, our approach achieved the average Dice scores of 77.63, 71.01, and 81.11% for ROSE-1, ROSE-2, and OCTA-Z,respectively. The experimental results demonstrated that the proposed network effectively learned and utilized implicit vessel priors for OCTA vessel segmentation. Extensive evaluations indicated that our network surpassed the current state-of-the-art methods. Specifically, our reconstruction approach of applying latent prior tokens offered a promising solution for the pattern representation of retinal vessels. In the future, we will extend this method to support more comprehensive retinal structure segmentation and eye-related disease classification tasks. Additionally, we plan to integrate diverse and detailed prior knowledge, such as anatomical and pathological information, to enhance the performance and versatility of ourapproach.

Automated blood vessel segmentation of retinal images offers huge potential benefits for medical diagnosis of different ocular diseases. In this paper, 2D Matched Filters (MF) are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Due to the Gaussian nature of blood vessel profile, the MF with Gaussian kernel often misclassifies non-vascular structures (e.g., step, ramp or other transients) as vessels. To avoid such false detection, this paper introduces Laplacian of Gaussian (LoG) filters in the vessel segmentation process. The inherent zero-crossing property of LoG filter is used in the algorithm, along with the MF, in order to extract vessels reliably from the retinal images. The proposed method is validated against three publicly available databases, STARE, DRIVE and HRF. Simulation results show that the proposed method is able to segment vessels accurately from the three database images with an average accuracy that is competitive to the existing methodologies.

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose two automatic blood vessel segmentation methods. The first proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the contents of several binary images resulting from vessel width dependent modified morphological filters on normalized retinal images. In the second proposed algorithm the blood vessel is segmented using normalized modified morphological operations and neuro fuzzy classifier. Normalized morphological operations are used to enhance the vessels and neuro fuzzy classifier is used to segment retinal blood vessels. These methods are applied on the publicly available DRIVE database and the experimental results obtained by using green channel images have been presented and their results are compared with recently published methods. The results demonstrate that our algorithms are very effective methods to detect retinal blood vessels. DOI: http://dx.doi.org/10.11591/ijece.v4i3.5829

FRE-GAN : Full-resolution efficient convolutional generative adversarial network for retinal vessel segmentation.

Accurate segmentation of retinal blood vessels plays a vital role in medical image analysis for the diagnosis and management of eye diseases, as most health conditions affect the retinal vascular network.Despite progress made in recent decades, issues such as complex textures, vessel discontinuities, and unidentified areas remain unresolved, particularly in segmenting fine veins and handling low-contrast images.This method integrates three advanced techniques for retinal vessel segmentation.Initially, it applies Adaptive Gradient Domain Guided Image Filtering (AGD-GIF) for preprocessing.Subsequently, it utilizes an efficient feature extraction method called the proposed Cascading Residual Graph Convolutional Network (CRGCN).Finally, segmentation is performed using an enhanced Mask Region-Based Convolutional Neural Network with a Parrot Optimizer (Improved Mask R-CNN-PO), ensuring precision and reliability in detecting retinal vessels from medical images.When tested on the CHASE_DB1 datasets, the proposed model achieves specificity of 99.53%, accuracy of 99.65%, and an AUC of 99%.Additionally, it attains an accuracy of 99.63% and F1 scores of 99.59%.

The segmentation of fundus vascular images is a key step in retinal image processing and analysis. It plays an important role in the early prevention and diagnosis of eye diseases and blood system diseases. In view of the low accuracy of retinal blood vessel segmentation caused by the complex features of fundus images, the presence of soft, hard penetrant and bleeding noises, the low contrast between retinal blood vessels and background, uneven changes in width and curvature, small shape and blurred edges, this paper proposes a new network model RCU-Net for retinal vessel segmentation. Specifically, this paper proposes CBAM-Conv convolution block and RA-Conv convolution block based on visual attention mechanism (CBAM) and residual connection idea, which can make the network better use of the information at the spatial and channel levels. Experiments were carried out in public datasets DRIVE and CHASE_DB1. The accuracy, sensitivity, F1-Score and AUC of RCU-Net network on DRIVE datasets reached 96.75%, 82.02%, 81.45% and 98.33% respectively. Compared with the classical U-Net network, the proposed model can effectively improve the performance of retinal vessel segmentation.

The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710.

An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering

Accurate medical imaging segmentation of the retinal fundus vasculature is essential to assist physicians in diagnosis and treatment. In recent years, convolutional neural networks (CNN) are widely used to classify retinal blood vessel pixels for retinal blood vessel segmentation tasks. However, the convolutional block receptive field is limited, simple multiple superpositions tend to cause information loss, and there are limitations in feature extraction as well as vessel segmentation. To address these problems, this paper proposes a new retinal vessel segmentation network based on U-Net, which is called multi-scale cross-position attention network (MCPANet). MCPANet uses multiple scales of input to compensate for image detail information and applies to skip connections between encoding blocks and decoding blocks to ensure information transfer while effectively reducing noise. We propose a cross-position attention module to link the positional relationships between pixels and obtain global contextual information, which enables the model to segment not only the fine capillaries but also clear vessel edges. At the same time, multiple scale pooling operations are used to expand the receptive field and enhance feature extraction. It further reduces pixel classification errors and eases the segmentation difficulty caused by the asymmetry of fundus blood vessel distribution. We trained and validated our proposed model on three publicly available datasets, DRIVE, CHASE, and STARE, which obtained segmentation accuracy of 97.05%, 97.58%, and 97.68%, and Dice of 83.15%, 81.48%, and 85.05%, respectively. The results demonstrate that the proposed method in this paper achieves better results in terms of performance and segmentation results when compared with existing methods.

Objective. Retinal vessel segmentation from optical coherence tomography angiography (OCTA) volumes is significant in analyzing blood supply structures and the diagnosing ophthalmic diseases. However, accurate retinal vessel segmentation in 3D OCTA remains challenging due to the interference of choroidal blood flow signals and the variations in retinal vessel structure. Approach. This paper proposes a layer attention network (LA-Net) for 3D-to-2D retinal vessel segmentation. The network comprises a 3D projection path and a 2D segmentation path. The key component in the 3D path is the proposed multi-scale layer attention module, which effectively learns the layer features of OCT and OCTA to attend to the retinal vessel layer while suppressing the choroidal vessel layer. This module also efficiently captures 3D multi-scale information for improved semantic understanding during projection. In the 2D path, a reverse boundary attention module is introduced to explore and preserve boundary and shape features of retinal vessels by focusing on non-salient regions in deep features. Main results. Experimental results in two subsets of the OCTA-500 dataset showed that our method achieves advanced segmentation performance with Dice similarity coefficients of 93.04% and 89.74%, respectively. Significance. The proposed network provides reliable 3D-to-2D segmentation of retinal vessels, with potential for application in various segmentation tasks that involve projecting the input image. Implementation code: https://github.com/y8421036/LA-Net.

Retinal fundus image is a crucial tool for ophthalmologists to diagnose eye-related diseases. These images provide visual information of the interior layer of the retina structures such as optic disc, optic cup, blood vessels and macula that can assist ophthalmologist in determining the health of an eye. Segmentation of blood vessels in fundus images is one of the most fundamental phase in detecting diseases such as diabetic retinopathy. However, the ambiguity of the retina structures in the retinal fundus images presents a challenge for researcher to segment the blood vessels. Extensive pre-processing and training of the images is necessary for precise segmentation, which is very intricate and laborious. This paper proposes the implementation of object-oriented-based metadata (OOM) structures of each pixel in the retinal fundus images. These structures comprise of additional metadata towards the conventional red, green, and blue data for each pixel within the images. The segmentation of the blood vessels in the retinal fundus images are performed by considering these additional metadata that enunciates the location, color spaces, and neighboring pixels of each individual pixel. From the results, it is shown that accurate segmentation of retinal fundus blood vessels can be achieved by purely employing straightforward thresholding method via the OOM structures without extensive pre-processing image processing technique or data training.