STARE Datasets Research Articles

A High-Resolution Network with Strip Attention for Retinal Vessel Segmentation

Accurate segmentation of retinal vessels is an essential prerequisite for the subsequent analysis of fundus images. Recently, a number of methods based on deep learning have been proposed and shown to demonstrate promising segmentation performance, especially U-Net and its variants. However, tiny vessels and low-contrast vessels are hard to detect due to the issues of a loss of spatial details caused by consecutive down-sample operations and inadequate fusion of multi-level features caused by vanilla skip connections. To address these issues and enhance the segmentation precision of retinal vessels, we propose a novel high-resolution network with strip attention. Instead of the U-Net-shaped architecture, the proposed network follows an HRNet-shaped architecture as the basic network, learning high-resolution representations throughout the training process. In addition, a strip attention module including a horizontal attention mechanism and a vertical attention mechanism is designed to obtain long-range dependencies in the horizontal and vertical directions by calculating the similarity between each pixel and all pixels in the same row and the same column, respectively. For effective multi-layer feature fusion, we incorporate the strip attention module into the basic network to dynamically guide adjacent hierarchical features. Experimental results on the DRIVE and STARE datasets show that the proposed method can extract more tiny vessels and low-contrast vessels compared with existing mainstream methods, achieving accuracies of 96.16% and 97.08% and sensitivities of 82.68% and 89.36%, respectively. The proposed method has the potential to aid in the analysis of fundus images.

Automated diagnostic classification of diabetic retinopathy with microvascular structure of fundus images using deep learning method

Multi-class classification is a major concern in the research field, especially in medical image analysis. This work proposes a novel method for automatically segmenting the blood vessels and classifying Diabetic Retinopathy (DR) using fundus images. This system helps ophthalmologists with the early detection and grading of DR diseases. The main aim here is to detect any pathological changes happening in the retinal vascular structure for the development of DR, through which the patients can avoid undergoing expensive scans. The proposed system involves three different stages, including pre-processing, vessel segmentation, and classification. The input images are processed first to eliminate the noise, followed by green channel extraction and enhancement with Contrast Limited Adaptive Histogram Equalization (CLAHE) and gamma correction. Retinal Vascular Structure (RVS) segmentation is a major concern in this work as it is responsible for detecting the different stages of DR by detecting the presence of microaneurysms, haemorrhages, and exudates. The U-Net is used as a base architecture to develop the segmentation model. The contracting path in the U-Net contains four consecutive downsampling and upsampling layers with skip connections. After performing downsampling four times, information on the tiny blood vessels may be missed. Therefore, ResEAD2Net is introduced in this work, where the number of downsampling and upsampling layers is reduced to two instead of four, and two such contracting paths and expansion paths are added to the network. Thus, detailed semantic information can be retained with this structure. Residual blocks are included instead of convolution blocks to increase the computational speed. To include structural connectivity, the segmented output is passed through the proposed Regularized Random Walker (RRW) algorithm, which focuses on the broken blood vessels. Finally, the features are extracted from the vessel structure and passed through the Machine Learning (ML) classifier to predict the DR grading. The proposed method achieves better performance in segmentation with accuracy, sensitivity, specificity, and area under the curve values of 98.07%, 90.24%, 99.01%, and 97.51%, respectively, for the STARE dataset and 97.55, 90.07, 98.01, and 97.64, respectively, for the DRIVE dataset. It is also found that the features obtained from the segmentation and ML classifier outperforms the existing methods in multiclass classification by achieving accuracy, sensitivity, and specificity of 98.88%, 98.91%, and 98.29% with the Messidor-2 dataset.

Multi-level spatial-temporal and attentional information deep fusion network for retinal vessel segmentation

Objective. Automatic segmentation of fundus vessels has the potential to enhance the judgment ability of intelligent disease diagnosis systems. Even though various methods have been proposed, it is still a demanding task to accurately segment the fundus vessels. The purpose of our study is to develop a robust and effective method to segment the vessels in human color retinal fundus images. Approach. We present a novel multi-level spatial-temporal and attentional information deep fusion network for the segmentation of retinal vessels, called MSAFNet, which enhances segmentation performance and robustness. Our method utilizes the multi-level spatial-temporal encoding module to obtain spatial-temporal information and the Self-Attention module to capture feature correlations in different levels of our network. Based on the encoder and decoder structure, we combine these features to get the final segmentation results. Main results. Through abundant experiments on four public datasets, our method achieves preferable performance compared with other SOTA retinal vessel segmentation methods. Our Accuracy and Area Under Curve achieve the highest scores of 96.96%, 96.57%, 96.48% and 98.78%, 98.54%, 98.27% on DRIVE, CHASE_DB1, and HRF datasets. Our Specificity achieves the highest score of 98.58% and 99.08% on DRIVE and STARE datasets. Significance. The experimental results demonstrate that our method has strong learning and representation capabilities and can accurately detect retinal blood vessels, thereby serving as a potential tool for assisting in diagnosis.

A multi-scale global attention network for blood vessel segmentation from fundus images

Accurate segmentation of retinal fundus vessel images is vital to clinical diagnosis. Due to the intricate vascular morphology, high noise and low contrast of fundus vessel images, retinal fundus vessel segmentation is still a challenging task, especially for thin vessel segmentation. In recent years, on account of strong context feature extraction ability of deep learning, it has shown a remarkable performance in the automatic segmentation of retinal fundus vessels. However, it still exhibits certain limitations, such as information loss on micro objects or details, inadequate treatment of local features, etc. Faced with these challenging factors, we present a new multi-scale global attention network (MGA-Net). To realize effective feature representation, a dense attention U-Net network is proposed. Meanwhile, we design a global context attention (GCA) block to realize multi-scale feature fusion, allowing the global features from the deep network layers to flow to the shallow network layers. Further, aimed at retinal fundus vessel segmentation task again the class imbalance issue, the AG block is also introduced. Related experiments are conducted on CHASE_DB1, DRIVE and STARE datasets to show the effectiveness of proposed segmentation model. The experimental results demonstrate the robustness of the proposed method with F1 exceeding 82% on all three datasets and effectively improve the segmentation performance of thin vessels. The source code of proposed MGA-Net is available at https://github.com/gegao310/workspace.git.

Multi-OCDTNet: A Novel Multi-Scale Object Context Dilated Transformer Network for Retinal Blood Vessel Segmentation

Image segmentation is an essential part of medical image processing, which plays a significant role in adjunctive therapy, disease diagnosis, and medical assessment. To solve the problem of insufficient extracting context information, especially for medical image segmentation, this paper proposes a novel network architecture of multi-scale object context dilated transformer network (Multi-OCDTNet) to improve the utilization and segmentation accuracy for context information. The multi-scale object context transformer module can extract the multi-scale context information of the image through a three-layer transformer structure in a parallel way. The dilated convolution self-aware module can enhance the awareness of multi-scale context information in the feature map through layering transformer block groups and a set of transformer layers. In addition, we propose a composite weight-assigned-based loss function based on DDCLoss and Focal Tversky Loss to improve the stability of the segmentation performance of Multi-OCDTNet by adjusting the weight. The performance of Multi-OCDTNet is validated on the DRIVE and STARE datasets with segmentation accuracy of 97.17% and 97.84%, respectively, indicating the Multi-OCDTNet network possesses a significant competitive advantage in improving the segmentation performance of retinal vessel images.

Scale-aware dense residual retinal vessel segmentation network with multi-output weighted loss

BackgroundRetinal vessel segmentation provides an important basis for determining the geometric characteristics of retinal vessels and the diagnosis of related diseases. The retinal vessels are mainly composed of coarse vessels and fine vessels, and the vessels have the problem of uneven distribution of coarse and fine vessels. At present, the common retinal blood vessel segmentation network based on deep learning can easily extract coarse vessels, but it ignores the more difficult to extract fine vessels.MethodsScale-aware dense residual model, multi-output weighted loss and attention mechanism are proposed and incorporated into the U-shape network. The model is proposed to extract image features through residual module, and using a multi-scale feature aggregation method to extract the deep information of the network after the last encoder layer, and upsampling output at each decoder layer, compare the output results of each decoder layer with the ground truth separately to obtain multiple output losses, and the last layer of the decoder layers is used as the final prediction output.ResultThe proposed network is tested on DRIVE and STARE. The evaluation indicators used in this paper are dice, accuracy, mIoU and recall rate. On the DRIVE dataset, the four indicators are respectively 80.40%, 96.67%, 82.14% and 88.10%; on the STARE dataset, the four indicators are respectively 83.41%, 97.39%, 84.38% and 88.84%.ConclusionThe experiment result proves that the network in this paper has better performance, can extract more continuous fine vessels, and reduces the problem of missing segmentation and false segmentation to a certain extent.

Enhancing fine retinal vessel segmentation: Morphological reconstruction and double thresholds filtering strategy.

Eye diseases such as diabetic retinopathy are progressive with various changes in the retinal vessels, and it is difficult to analyze the disease for future treatment. There are many computerized algorithms implemented for retinal vessel segmentation, but the tiny vessels drop off, impacting the performance of the overall algorithms. This research work contains the new image processing techniques such as enhancement filters, coherence filters and binary thresholding techniques to handle the different color retinal fundus image problems to achieve a vessel image that is well-segmented, and the proposed algorithm has improved performance over existing work. Our developed technique incorporates morphological techniques to address the center light reflex issue. Additionally, to effectively resolve the problem of insufficient and varying contrast, our developed technique employs homomorphic methods and Wiener filtering. Coherent filters are used to address the coherence issue of the retina vessels, and then a double thresholding technique is applied with image reconstruction to achieve a correctly segmented vessel image. The results of our developed technique were evaluated using the STARE and DRIVE datasets and it achieves an accuracy of about 0.96 and a sensitivity of 0.81. The performance obtained from our proposed method proved the capability of the method which can be used by ophthalmology experts to diagnose ocular abnormalities and recommended for further treatment.

Landmark-Assisted Anatomy-Sensitive Retinal Vessel Segmentation Network.

Automatic retinal vessel segmentation is important for assisting clinicians in diagnosing ophthalmic diseases. The existing deep learning methods remain constrained in instance connectivity and thin vessel detection. To this end, we propose a novel anatomy-sensitive retinal vessel segmentation framework to preserve instance connectivity and improve the segmentation accuracy of thin vessels. This framework uses TransUNet as its backbone and utilizes self-supervised extracted landmarks to guide network learning. TransUNet is designed to simultaneously benefit from the advantages of convolutional and multi-head attention mechanisms in extracting local features and modeling global dependencies. In particular, we introduce contrastive learning-based self-supervised extraction anatomical landmarks to guide the model to focus on learning the morphological information of retinal vessels. We evaluated the proposed method on three public datasets: DRIVE, CHASE-DB1, and STARE. Our method demonstrates promising results on the DRIVE and CHASE-DB1 datasets, outperforming state-of-the-art methods by improving the F1 scores by 0.36% and 0.31%, respectively. On the STARE dataset, our method achieves results close to the best-performing methods. Visualizations of the results highlight the potential of our method in maintaining topological continuity and identifying thin blood vessels. Furthermore, we conducted a series of ablation experiments to validate the effectiveness of each module in our model and considered the impact of image resolution on the results.

Robust blood vessel detection with image enhancement using relative intensity order transformation and deep learning

Background:An eye-related diabetic microvascular condition is called diabetic retinopathy (DR). Injury to the retinal blood vessels, one of the main causes of blindness worldwide, is a severe public health problem. The vessel growth within the retina, and the likelihood of change in the estimation of retinal veins are important for understanding the stage of diabetic retinopathy. Segmenting retinal blood vessels is necessary to see the changes. Purpose: To develop a framework to improve the quality of the segmentation findings over diabetic retinal datasets. Method:Deep learning-based approaches now show outstanding performance on blood vessel segmentation. However, the majority of them concentrate on designing powerful deep learning architectures rather than capturing the underlying curvilinear structure feature (e.g., the curvilinear structure is darker than the background). In this paper, a robust blood vessel segmentation technique is proposed that captures the characteristics of the blood vessel using relative intensity order transformations(RIOT) and statistical edge-based features. The RIOT extract the thick vessels considering 16 pixels on the horizontal and vertical directions, and the statistical edge features are used to extract the thin blood vessels. The proposed method balances the extraction of thin and thick blood vessels providing robust blood vessel segmentation insensitive to contrast variance in images. Results:The proposed method is validated on the DRIVE, CHASEDB1 and STARE datasets. The proposed model achieved a segmentation accuracy of 96% on the DRIVE dataset, 99% on CHASEDB1 dataset and 87% on STARE dataset. Conclusions:The paper has presented a framework that is contrast invariant in extracting the thick blood vessels using RIOT and thin vessels using statistical edge features and proposed framework has outperformed the existing models.

Diabetic retinopathy feature extraction images based on confusion neural network

<p class="15">The diagnosis of diabetic retinopathy depends on the evaluation of retinal fundus pictures. The current methods have been successful in extracting features from fundus images, but due to the complex blood vessel distribution in these images and the presence of a great deal of noise, simple methods based on threshold segmentation and clustering are vulnerable to feature loss during the extraction process. For example, the small blood vessels in the fundus are lost, and the branches of blood vessels are blurred. In addition, the noise in medical images is mainly distributed in the high-frequency area of the image. The proposed method to segment the retinal fundus vessels in the DRIVE and STARE datasets, the average accuracy of this method is 95.45% and 94.81%, respectively, and the sensitivity and specificity are 73.35%, 75.39% and 97.34%, 95.75%. In addition, compared with related methods, the proposed method has higher segmentation accuracy, and after segmentation, the fundus blood vessels have higher integrity, clear structure, and less loss of small blood vessels.</p>

Linear-exponential loss incorporated deep learning for imbalanced classification

The class imbalance issue is a pretty common and enduring topic all the time. When encountering unbalanced data distribution, conventional methods are prone to classify minority samples as majority ones, which may cause severe consequences in reality. It is crucial yet challenging to cope with such problems. In this paper, inspired by our previous work, we borrow the linear-exponential (LINEX) loss function in statistics into deep learning for the first time and extend it into a multi-class form, denoted as DLINEX. Compared with existing loss functions in class imbalance learning (e.g., the weighted cross entropy-loss and the focal loss), DLINEX has an asymmetric geometry interpretation, which can adaptively focus more on the minority and hard-to-classify samples by solely adjusting one parameter. Besides, it simultaneously achieves between and within class diversities via caring about the inherent properties of each instance. As a result, DLINEX achieves 42.08% G-means on the CIFAR-10 dataset at the imbalance ratio of 200, 79.06% G-means on the HAM10000 dataset, 82.74% F1 on the DRIVE dataset, 83.93% F1 on the CHASEDB1 dataset and 79.55% F1 on the STARE dataset The quantitative and qualitative experiments convincingly demonstrate that DLINEX can work favorably in imbalanced classifications, either at the image-level or the pixel-level.

RetU-Net: An Enhanced U-Net Architecture for Retinal Lesion Segmentation

Diabetic retinopathy is a predominant vision-threatening disease affecting working-aged people specifically. Timely diagnosis through early detection and prevention helps to reduce the risk of severe vision loss. Computer-aided diagnosis in retinal image analysis through Machine Learning techniques will help medical professionals perform their analysis better. Automated image processing through Convolutional Neural Networks has proven to be a promising technique, mainly in medical image segmentation. Convolutional Neural Network techniques like 3D CNN, Deep CNN and architectures like U-Net, V-Net, SegNet, and DeepMedic have outperformed medical image analysis results. However, the baseline CNN architectures struggle to retain high-quality information at the output and thus affect the performance. This work focuses on addressing the issue of translational variance and overfitting scenarios of U-Net architecture by experimenting with adaptable window sizes, pretrained weights and linear interpolation technique. A novel U-Net based architecture called RetU-Net that segments abnormal retinopathy lesion structures by retaining higher-level feature is proposed. The experiments are conducted using Indian Diabetic Retinopathy Image Dataset provided in “Diabetic Retinopathy: Segmentation and Grading Challenge” initially. The results have been compared with other state-of-art CNN architectures. Further evaluation is carried out on public datasets: STARE and DRIVE, and the performance is compared with U-Net based architectures used for retinal image segmentation. The proposed approach is efficient in terms of precision, sensitivity, specificity and accuracy. It received accuracy scores of 0.9612, 0.9712 while experimenting with STARE and DRIVE datasets respectively.

Robust retinal blood vessel segmentation using a patch-based statistical adaptive multi-scale line detector

The diagnosis of diseases using the retinal vasculature has long been a focus of medical research. Segmenting the vessels from retinal images is still a difficult task because of the variability in retinal vascular thickness and the inhomogeneity of image intensity. In this research work, a two-stage retinal vessel segmentation method is proposed. At the first stage, the large vessels are segmented by using multi-scale line detection by suppressing the small vessels through non-local mean filtering. At the second stage, the image is enhanced through noise suppression attributed largely to the block-matching 3-D filtering inside the nonsubsampled contourlet transform. Then a patch-based multi-scale line detector with adaptive statistical thresholding is used. Finally, both large and small vessels are combined to reconstruct the final vessel segmented image. The proposed method is evaluated on two publicly available databases and achieves an average sensitivity of 82.61%, and 81.67% on the DRIVE and STARE datasets respectively. The proposed method outperforms other state-of-the-art methods and has proven to be effective in boosting the sensitivity of retinal vessel segmentation.

Retinal Image Segmentation and Disease Classification using Deep Learning

Abstract: Segmenting and classifying retinal blood vessels are essential ophthalmology tasks because they reveal important details regarding the health and condition of the retina and its blood vessels. However, there is still room for improvement in terms of accuracy and robustness. Deep learning models have demonstrated significant promise in precisely and quickly segmenting and categorizing retinal pictures. In this study, we suggest the Residual U-Net (ResUNet) model for segmenting retinal arteries and the Resnet50 model for classifying retinal arteries. For enhanced feature extraction and picture segmentation, the ResUNet model, a deep learning architecture, combines the benefits of the U-Net and ResNet50 models. Accuracy was required for segmentation and classification, which gave our study's focus on the project and potential survey results in its distinctiveness. The DRIVE, CHASE-DB1, STARE, and HRF datasets, which include retinal pictures with ground truth annotations, were used to train and test our model. These data were the resource for obtaining the segmented output with analysis in accordance with the initial input supplied to the model. Another sign of the robustness of our model is its strong generalization capacity. Overall, our study shows the usefulness of the ResUNet model for retinal vascular segmentation and classification and highlights its potential for enhancing the diagnosis and treatment of retinal illnesses, which has the potential to improve further with the potential for further development.

Attention aware fully convolutional deep learning model for retinal blood vessel segmentation

Recent reports indicate a rise in retinal issues, and automatic artery vein categorization offers data that is particularly instructive for the medical evaluation of serious retinal disorders including glaucoma and diabetic retinopathy. This work presents a competent and precise deep-learning model designed for vessel segmentation in retinal fundus imaging. This article aims to segment the retinal images using an attention-based dense fully convolutional neural network (A-DFCNN) after removing uncertainty. The artery extraction layers encompass vessel-specific convolutional blocks to focus the tiny blood vessels and dense layers with skip connections for feature propagation. Segmentation is associated with artery extraction layers via individual loss function. Blood vessel maps produced from individual loss functions are authenticated for performance. The proposed technique attains improved outcomes in terms of Accuracy (0.9834), Sensitivity (0.8553), and Specificity (0.9835) from DRIVE, STARE, and CHASE-DB1 datasets. The result demonstrates that the proposed A-DFCNN is capable of segmenting minute vessel bifurcation breakdowns during the training and testing phases.

Identification of retinal diseases based on retinal blood vessel segmentation using Dagum PDF and feature-based machine learning

ABSTRACT Retinal diseases is one of the major cause of vision impairments worldwide and the variations in fundus images help to identify these retinal diseases. Manual identification and segmentation of these fundus images is a tedious task and is highly prone to errors. Thus, a Dagum probability distribution function (PDF) based on matched filter (MF) for retinal blood vessel segmentation is proposed in this work. Initially, different features are extracted from the fundus images and these features are provided to the ensemble classifier to perform the task of disease classification. The overall performance of the proposed work is evaluated using different popular datasets such as DRIVE, STARE, HRF, IRDiD, Kaggle retinal, ORIGA and RFI. It is found that the specificity of 0.9802 and 0.9838 is achieved on DRIVE and STARE datasets respectively. In addition, the input image is predicted with HRF-glaucoma image ‘10_g.jpg’, having a higher prediction accuracy of 95.45%.

A Convolutional Autoencoder Approach for Boosting the Specificity of Retinal Blood Vessels Segmentation

Automated retina vessel segmentation of the human eye plays a vital role as it can significantly assist ophthalmologists in identifying many eye diseases, such as diabetes, stroke, arteriosclerosis, cardiovascular disease, and many other human illnesses. The fast, automatic and accurate retina vessel segmentation of the eyes is very desirable. This paper introduces a novel fully convolutional autoencoder for the retina vessel segmentation task. The proposed model consists of eight layers, each consisting of convolutional2D layers, MaxPooling layers, Batch Normalisation layers and more. Our model has been trained and evaluated on DRIVE and STARE datasets with 35 min of training time. The performance of the autoencoder model we introduce is assessed on two public datasets, the DRIVE and the STARE and achieved quite competitive results compared to the state-of-the-art methods in the literature. In particular, our model reached an accuracy of 95.73, an AUC_ROC of 97.49 on the DRIVE dataset, and an accuracy of 96.92 and an AUC ROC of 97.57 on the STARE dataset. Furthermore, our model has demonstrated the highest specificity among the methods in the literature, reporting a specificity of 98.57 on the DRIVE and 98.7 on the STARE dataset, respectively. The above statement can be noticed in the final blood vessel segmentation images produced by our convolutional autoencoder method since the segmentations are more accurate, sharp and noiseless than the result images of other proposed methods.

DA-Res2UNet: Explainable blood vessel segmentation from fundus images

Blood vessel segmentation in fundus images is necessary for the diagnosis of ophthalmic diseases. In recent years, deep learning has achieved eminent performance in blood vessel segmentation, and there still exist challenges to reduce misidentification and improve microvascular segmentation accuracy. One reason is that traditional Convolutional Neural Network (CNN) can not effectively extract multiscale information and discard the unnecessary information. Another reason is we can’t explain why some blood vessels fail to be identified. On the one hand, this paper proposes a Dual Attention Res2UNet (DA-Res2UNet) model. The DA-Res2UNet model uses Res2block rather than CNN to obtain more multiscale information and adds Dual Attention to help the model focus on important information and discard unnecessary information. On the other hand, the explainable method based on a pre-trained fundus image generator is adopted to explore how the model identifies blood vessels. We deduce several special situations that lead to the misidentification based on the model’s explanation and adjust the dataset for these special cases. The adjusted datasets significantly reduce the misidentification in the CHASE_DB1 dataset. Finally, the model trained by the adjusted datasets achieves the state-of-the-art F1-score of 81.88%, 82.77%, and 83.96% on the CHASE_DB1, DRIVE and STARE datasets, respectively.

Application of Novel Down-sampling Method in Retinal Vessel Segmentation

Accurate segmentation of retinal blood vessels is of great significance for diagnosing, preventing and detecting eye diseases. In recent years, the U-Net network and its various variants have reached advanced level in the field of medical image segmentation. Most of these networks choose to use simple max pooling to down-sample the intermediate feature layer of the image, which is easy to lose part of the information, so this study proposes a simple and effective new down-sampling method Pixel Fusion-pooling (PF-pooling), which can well fuse the adjacent pixel information of the image. The down-sampling method proposed in this study is a lightweight general module that can be effectively integrated into various network architectures based on convolutional operations. The experimental results on the DRIVE and STARE datasets show that the F1-score index of the U-Net model using PF-pooling on the STARE dataset improved by 1.98%. The accuracy rate is increased by 0.2%, and the sensitivity is increased by 3.88%. And the generalization of the proposed module is verified by replacing different algorithm models. The results show that PF-pooling has achieved performance improvement in both Dense-UNet and Res-UNet models, and has good universality.

Feature preserving mesh network for semantic segmentation of retinal vasculature to support ophthalmic disease analysis.

Ophthalmic diseases are approaching an alarming count across the globe. Typically, ophthalmologists depend on manual methods for the analysis of different ophthalmic diseases such as glaucoma, Sickle cell retinopathy (SCR), diabetic retinopathy, and hypertensive retinopathy. All these manual assessments are not reliable, time-consuming, tedious, and prone to error. Therefore, automatic methods are desirable to replace conventional approaches. The accuracy of this segmentation of these vessels using automated approaches directly depends on the quality of fundus images. Retinal vessels are assumed as a potential biomarker for the diagnosis of many ophthalmic diseases. Mostly newly developed ophthalmic diseases contain minor changes in vasculature which is a critical job for the early detection and analysis of disease. Several artificial intelligence-based methods suggested intelligent solutions for automated retinal vessel detection. However, existing methods exhibited significant limitations in segmentation performance, complexity, and computational efficiency. Specifically, most of the existing methods failed in detecting small vessels owing to vanishing gradient problems. To overcome the stated problems, an intelligence-based automated shallow network with high performance and low cost is designed named Feature Preserving Mesh Network (FPM-Net) for the accurate segmentation of retinal vessels. FPM-Net employs a feature-preserving block that preserves the spatial features and helps in maintaining a better segmentation performance. Similarly, FPM-Net architecture uses a series of feature concatenation that also boosts the overall segmentation performance. Finally, preserved features, low-level input image information, and up-sampled spatial features are aggregated at the final concatenation stage for improved pixel prediction accuracy. The technique is reliable since it performs better on the DRIVE database, CHASE-DB1 database, and STARE dataset. Experimental outcomes confirm that FPM-Net outperforms state-of-the-art techniques with superior computational efficiency. In addition, presented results are achieved without using any preprocessing or postprocessing scheme. Our proposed method FPM-Net gives improvement results which can be observed with DRIVE datasets, it gives Se, Sp, and Acc as 0.8285, 0.98270, 0.92920, for CHASE-DB1 dataset 0.8219, 0.9840, 0.9728 and STARE datasets it produces 0.8618, 0.9819 and 0.9727 respectively. Which is a remarkable difference and enhancement as compared to the conventional methods using only 2.45 million trainable parameters.