MSR U-Net: An Improved U-Net Model for Retinal Blood Vessel Segmentation

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.

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 retinal vessel segmentation task plays an important role in clinical diagnosis and treatment, especially in cardiovascular diseases such as diabetic retinopathy and hypertensive retinopathy. Recently, the fully convolutional neural network, as a popular learning-based segmentation method, has been demonstrated to yield highly segmentation performance in vessel wall segmentation tasks. However, the major network factors affecting the performance of segmentation are still not obvious. This paper uses the single-factor control variable method to investigates the effects of network architectures (FCN network and U-Net network) and other network factors (pooling times, patch size, number of skip connection, and network depth) on retinal blood vessel segmentation. Our experiments are performed on two public fundus image database DRIVE and STARE. The results show that U-net is better than FCN and skip connections, proper pooling times, dilated convolution is vital to obtain better performance of retinal vessel segmentation.

In this research, retinal blood vessel fundus images will be segmented. Moreover, bifurcation point of the segmented blood vessel images will be determined. The data used for this research was obtained from fundus image database of DRIVE (Digital Retinal Images for Vessel Extraction) dataset. The preprocessing phases contain of several steps such as green channel extraction, histogram equalization and optic disc elimination. Meanwhile, the segmentation phase was performed by combining two filtering methods. These methods are median and derivative of Gaussian filter. Median filter was used to reduce the noise on retinal fundus image such as hard and soft exudates as well as dot and blot hemorrhages. Combination of these two filtering methods were also employed to determine the bifurcation point of segmented blood vessel image. The bifurcation points and the result from blood vessel segmentation were among two of the unique parameters of retinal fundus image. These parameters will be used eventually as unique features of the advanced research which is a biometric system to identify an individual based on his/her unique retinal pattern.

Fractional and B-COSFIRE filter based approach for efficient segmentation of retinal blood vessels

Automated retinal blood vessel segmentation in fundus images provides important evidence to ophthalmologists in coping with prevalent ocular diseases in an efficient and non-invasive way. However, segmenting blood vessels in fundus images is a challenging task, due to the high variety in scale and appearance of blood vessels and the high similarity in visual features between the lesions and retinal vascular. Inspired by the way that the visual cortex adaptively responds to the type of stimulus, we propose a Stimulus-Guided Adaptive Transformer Network (SGAT-Net) for accurate retinal blood vessel segmentation. It entails a Stimulus-Guided Adaptive Module (SGA-Module) that can extract local-global compound features based on inductive bias and self-attention mechanism. Alongside a light-weight residual encoder (ResEncoder) structure capturing the relevant details of appearance, a Stimulus-Guided Adaptive Pooling Transformer (SGAP-Former) is introduced to reweight the maximum and average pooling to enrich the contextual embedding representation while suppressing the redundant information. Moreover, a Stimulus-Guided Adaptive Feature Fusion (SGAFF) module is designed to adaptively emphasize the local details and global context and fuse them in the latent space to adjust the receptive field (RF) based on the task. The evaluation is implemented on the largest fundus image dataset (FIVES) and three popular retinal image datasets (DRIVE, STARE, CHASEDB1). Experimental results show that the proposed method achieves a competitive performance over the other existing method, with a clear advantage in avoiding errors that commonly happen in areas with highly similar visual features. The sourcecode is publicly available at: https://github.com/Gins-07/SGAT.

This paper deals with the retinal blood vessel segmentation in fundus video-sequences acquired by experimental fundus video camera. Quality of acquired video-sequences is relatively low and fluctuates across particular frames. Especially, due to the low resolution, poor signal-to-noise ratio, and varying illumination conditions within the frames, application of standard image processing methods might be difficult in such experimental fundus images. In this study, we tried two methods for the segmentation of retinal vessels - matched filtering and Hessian-based approach, originally developed for vessel segmentation in standard fundus images. We showed that modified versions of these two approaches, combined with support vector machine (SVM), can be used also for segmentation in experimental low-quality fundus video-sequences. The SVM classifier trained and consecutively tested on the database of high-resolution images achieved classification accuracy over 94 % and thus revealed a possible applicability of the proposed method on low-quality data. Then, testing on low-quality video-sequences revealed sufficiently large reliability in term of segmentation stability within the sequence with the interframe variability in image quality.

Segmentation of retinal blood vessels is a crucial preliminary step in the diagnosis of any retinal disease. While extracting vessel-map, the biggest challenge is to segment fine vessels which are in poor contrast with the non-vessel background. Key contribution of this work is a fine-to-coarse retinal vessel extraction model with high sensitivity. Proposed algorithm uses a directional wavelet to generate a novel multiscale, three-channel image. To generate this image, only the real coefficients of wavelet transform are used, which facilitate the extraction of fine vessel-ends. Multiple scales cover different thicknesses of vessels. The vessel-enhanced image catalyzes the learning of deep U-net model for pixel classification. This work uses STARE and DRIVE databases for experimentation. Algorithm has performed robustly well in cross-database testing, even in pathological environment. Proposed method has produced state-of-the-art results. The vessel segmentation is outstanding in terms of sensitivity measure which validates better extraction of fine vessels. In this paper, an elaborate comparison with the other existing methods is also presented.

<span>Segmentation of blood vessels (BVs) from retinal image is one of the important steps in developing a computer-assisted retinal diagnosis system and has been widely researched especially for implementing automatic BV segmentation methods. This paper proposes an improvement to an existing retinal BV (RBV) segmentation method by combining the trainable B-COSFIRE filter with adaptive thresholding methods. The proposed method can automatically configure its selectivity given a prototype pattern to be detected. Its segmentation performance is comparable to many published methods with the advantage of robustness against noise on retinal background. Instead of using grid search to find the optimal threshold value for a whole dataset, adaptive thresholding (AT) is used to determine the threshold for each retinal image. Two AT methods investigated in this study were ISODATA and Otsu’s method. The proposed method was validated using 40 images from two benchmark datasets for retinal BV segmentation validation, namely DRIVE and STARE. The validation results indicated that the segmentation performance of the proposed unsupervised method is comparable to the original B-COSFIRE method and other published methods, without requiring the availability of ground truth data for new dataset. The Sensitivity and Specificity values achieved for DRIVE and STARE are 0.7818, 0.9688, 0.7957 and 0.9648, respectively.</span>

We propose a lightweight multi-task deep learning architecture tailored for simultaneous retinal disease classification and blood vessel segmentation. Leveraging a shared Mo-bileNetV2 encoder, the model branches into task-specific heads for multi-class diabetic retinopathy (DR) grading and binary vessel segmentation. It is trained on the APTOS 2019 dataset for classification and the DRIVE dataset for segmentation using a label-masked, unified training strategy. The proposed model achieves 76.67% classification accuracy and a 0.5868 Dice score on segmentation, demonstrating competitive performance with significantly reduced computational complexity. Designed for real-time deployment on mobile devices, this model addresses the critical need for scalable, low-resource ophthalmic screening tools. Future work explores the integration of uncertainty estimation to enhance clinical interpretability and decision support.

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%.

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.

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.

Retinal vascular images play an essential role in the early diagnosis of ophthalmic diseases. Automatic segmentation of retinal blood vessels is a challenging task due to the complex vascular interlacing of fundus images and the noise problems during acquisition. Because of the above problems, we propose a new network structure, which is effective for vascular tree segmentation and can segment more fine blood vessels. Firstly, to improve the segmentation ability of the network to blood vessels, we design a novel convolutional structure of FMCD from the primary level. Then, to focus on the learning of vascular characteristics from the perspectives of deep segmentation and shallow segmentation, we design a deep-shallow dual-level network from the local level. Finally, to assist in supervising the deep and shallow networks to improve the segmentation accuracy of blood vessels and complement the segmentation output of the deep and shallow networks, to segment more microvessels, we use the multi-level semantic fusion module (MSFM) from the overall level. Our Acc, Sen, and F1 on the DRIVE dataset achieve high accuracy of 0.9706, 0.8404, and 0.8332, respectively, outperforming mainstream methods. The proposed method has important clinical implications.