Retinal Vessel Segmentation Algorithms Research Articles

Laddernet retinal vessel segmentation algorithm fusing attentional mechanisms

A retinal blood vessel segmentation algorithm fusing the attention mechanism is proposed to address the problems of insufficient accuracy and incorrect segmentation of minutiae in existing retinal blood vessel segmentation algorithms. The network structure used in this algorithm is Laddernet (LCT-UNet for short), which fuses the Convolutional Block Attention Module (CBAM) and Transformer, i.e., the residual module of Laddernet, which shares the weights, is structurally optimised to decrease the parameter count; After the residual module, CBAM is incorporated to adaptively adjust the weights of channels and spatial features to enhance the feature representation; Transformer with global self-attention mechanism is introduced at the bottom of the network to overcome the shortcomings of UNet's inability to model long-range relationships and spatial dependencies, and gradually refine the features so that the segmentation results have finer boundary and detail information. The model validity was verified on the public datasets DRIVE and CHASEDB1, and the experimental results showed that the ACC of LCT-UNet was 95.81% and 96.88%, SE was 79.74% and 81.12%, SP was 98.15% and 98.83%, and AUC was 98.22% and 98.99%, and the F1 values were 82.88% and 85.14%, respectively, and the comprehensive segmentation performance of the LCT-UNet network showed significant enhancement over the algorithms such as U-Net, R2UNet, and Laddernet.

Advancing Opthalmic Diagnostics: U-Net for Retinal Blood Vessel Segmentation

This research project focuses on the development and evaluation of an advanced algorithm for retinal vessel segmentation, a critical component in the automated analysis of retinal images for diagnosing ocular diseases. Leveraging state-of-the-art image processing techniques and deep learning models, we propose a novel segmentation algorithm that significantly enhances the accuracy and efficiency of identifying retinal blood vessels from fundus photographs. Our methodology encompasses a comprehensive data preparation phase, including image normalization and augmentation, to improve the model's robustness and generalizability. We implemented a convolutional neural network (CNN)-based architecture optimized for the intricate patterns and variations inherent in retinal images. The performance of our algorithm was rigorously evaluated against established benchmarks, demonstrating superior precision, recall, and a higher Dice coefficient compared to existing methods. These findings indicate the potential of our approach to contribute substantially to the early detection and monitoring of ocular conditions such as diabetic retinopathy and glaucoma. The research underscores the importance of advanced computational techniques in enhancing the diagnostic capabilities of retinal image analysis and sets the stage for future innovations in medical imaging.

Bi-directional ConvLSTM residual U-Net retinal vessel segmentation algorithm with improved focal loss function

Accurate blood vessel segmentation on retinal blood vessel images is helpful for the early detection of ophthalmic diseases such as diabetes, hypertension, cardiovascular and cerebrovascular diseases, and inhibits the deterioration of the disease. In current research within the field of retinal blood vessel segmentation, significant challenges exist in accurately segmenting small blood vessels and maintaining blood vessel continuity. The segmentation algorithm proposed in this article offers substantial improvements to address these issues. To enhance the segmentation performance of retinal blood vessels and facilitate more accurate diagnosis of fundus diseases by ophthalmologists, this paper introduces a novel bidirectional convolutional long short-term memory (LSTM) residual U-Net segmentation algorithm, incorporating improvements to the Focal loss function. Firstly, in the encoding part of U-Net, the multi-scale convolution kernels and Bi-ConvLSTM were adopted to improve the residual structure, obtain richer blood vessel features and enhance the detection ability of micro vessels and the continuity of blood vessel characteristics. At the same time, the class balanced cross entropy loss function was improved and the proportional modulation factor is introduced to enhance the learning ability of the network for difficult samples. By adding the Bi-ConvLSTM to the residual structure and introducing the proportional modulation coefficient to the loss function, the network structure realizes better feature information detection and greatly enhances the detection ability of small blood vessels. The experimental analysis on the DRIVE and CHASE_DB1 data sets showed that the sensitivity, specificity, accuracy and AUC reached 0.7961, 0.9796, 0.9563, 0.9792; 0.8344, 0.9665, 0.9547, 0.9758, respectively. The experimental results fully show that the Bi-ConvLSTM residual U-Net segmentation algorithm based on the improved Focal loss function enhances the detection ability of small blood vessel features, improves the continuity of blood vessel features and the network segmentation performance, and is superior to U-Net algorithm and some current mainstream retinal blood vessel segmentation algorithms.

Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network

Due to the limitations of traditional retinal blood vessel segmentation algorithms in feature extraction, vessel breakage often occurs at the end. To address this issue, a retinal vessel segmentation algorithm based on a modified U-shaped network is proposed in this paper. This algorithm can extract multi-scale vascular features and perform segmentation in an end-to-end manner. First, in order to improve the low contrast of the original image, pre-processing methods are employed. Second, a multi-scale residual convolution module is employed to extract image features of different granularities, while residual learning improves feature utilization efficiency and reduces information loss. In addition, a selective kernel unit is incorporated into the skip connections to obtain multi-scale features with varying receptive field sizes achieved through soft attention. Subsequently, to further extract vascular features and improve processing speed, a residual attention module is constructed at the decoder stage. Finally, a weighted joint loss function is implemented to address the imbalance between positive and negative samples. The experimental results on the DRIVE, STARE, and CHASE_DB1 datasets demonstrate that MU-Net exhibits better sensitivity and a higher Matthew’s correlation coefficient (0.8197, 0.8051; STARE: 0.8264, 0.7987; CHASE_DB1: 0.8313, 0.7960) compared to several state-of-the-art methods.

ProNet Adaptive Retinal Vessel Segmentation Algorithm Based on Improved UperNet Network

ProNet Adaptive Retinal Vessel Segmentation Algorithm Based on Improved UperNet Network

FSE-Net: Rethinking the up-sampling operation in encoder-decoder structure for retinal vessel segmentation

Automatic retinal vessel segmentation plays a crucial role in the diagnosis and assessment of various ophthalmologic diseases. Currently, the primary retinal vessel segmentation algorithms are based on the encoder-decoder structure. However, these U-Net analogs suffer from the loss of both spatial and semantic information, caused by continuous up-sampling operations in the decoder structure. In this paper, we rethink the above problem and build a novel deep neural network for retinal vessel segmentation, called FSE-Net. Specifically, to address the issue of feature information loss and enhance the performance of retinal vessel segmentation, we eliminate the decoder structure. In particular, we introduced a multi-head feature fusion block (MFF) as a substitute for the continuous up-sampling operation. Additionally, the encoder stage of FSE-Net incorporates a residual feature separable block (RFSB) to further refine and distill features, thereby enhancing the capability of feature extraction. Subsequently, we employ a residual atrous spatial feature aggregate module (RASF) to expand the network's receptive field by incorporating multi-scale feature information. We conducted experiments on five widely recognized databases for retinal vessel segmentation, namely DRIVE, CHASEDB1, STARE, IOSTAR, and LES-AV. The results demonstrate that our proposed FSE-Net outperforms state-of-the-art approaches in terms of segmentation performance. Moreover, we demonstrate the feasibility of achieving superior segmentation performance without employing the traditional U-Net analog network structure.

An improved supervised and attention mechanism-based U-Net algorithm for retinal vessel segmentation

The segmentation results of retinal blood vessels are crucial for automatically diagnosing ophthalmic diseases such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular diseases. To improve the accuracy of vessel segmentation and better extract information about small vessels and edges, we introduce the U-Net algorithm with a supervised attention mechanism for retinal vessel segmentation. We achieve this by introducing a decoder fusion module (DFM) in the encoding part, effectively combining different convolutional blocks to extract features comprehensively. Additionally, in the decoding part of U-Net, we propose the context squeeze and excitation (CSE) decoding module to enhance important contextual feature information and the detection of tiny blood vessels. For the final output, we introduce the supervised fusion mechanism (SFM), which combines multiple branches from shallow to deep layers, effectively fusing multi-scale features and capturing information from different levels, fully integrating low-level and high-level features to improve segmentation performance. Our experimental results on the public datasets of DRIVE, STARE, and CHASED_B1 demonstrate the excellent performance of our proposed network.

A Novel Hybrid Retinal Blood Vessel Segmentation Algorithm for Enlarging the Measuring Range of Dual-Wavelength Retinal Oximetry

The non-invasive measurement of hemoglobin oxygen saturation (SO2) in retinal vessels is based on spectrophotometry and the absorption spectral characteristics of the tissue. The dual-wavelength retinal images are simultaneously captured via retinal oximetry. SO2 is calculated by processing a series of images and by calculating the optic density ratio of two images. However, existing SO2 research is focused on the thick vessels in the high-clarity region of retinal images. However, the thin vessels in the low-clarity region could provide significant information for the detection and diagnosis of neovascular diseases. To this end, we proposed a novel hybrid vessel segmentation algorithm. Firstly, a median filter was employed for image denoising. Secondly, high- and low-clarity region segmentation was carried out based on a clarity histogram. The vessels in the high-clarity areas were segmented after implementing a Gaussian filter, a matched filter, and morphological segmentation. Additionally, the vessels in the low-clarity areas were segmented using a guided filter, matched filtering, and dynamic threshold segmentation. Finally, the results were obtained through image merger and morphological operations. The experimental results and analysis show that the proposed method can effectively segment thick and thin vessels and can extend the measuring range of dual-wavelength retinal oximetry.

A modified Coye algorithm for retinal vessel segmentation

According to a scientific study, eyes are the best predictors of numerous disorders including glaucoma, diabetic retinopathy, hypertension, and stroke. An ophthalmologist can learn about the problems by looking at the segmented retinal blood vessel network. The goal of this study is to offer ophthalmologists with reliable segmented retinal blood vessels to help them pinpoint the issue. This work put forwards an automated method of vessel extraction by incorporating curvelet-based enhancement with the Coye algorithm. Further, the segmentation performance is fine-tuned by embodying a pair of complementary gamma functions (PCGF) for contrast improvement. The suggested approach is evaluated on DRIVE and STARE databases and shows outstanding results as compared to state-of-the-art algorithms.

Hybrid algorithm for retinal blood vessel segmentation using different pattern recognition techniques

Blood vessel segmentation of the retina has become a necessary step in automatic disease identification and planning treatment in the field of Ophthalmology. To identify the disease properly, both thick and thin blood vessels should be distinguished clearly. Diagnosis of disease would be simple and easier only when the blood vessels are segmented accurately. Existing blood vessel segmentation methods are not supporting well to overcome the poor accuracy and low generalization problems because of the complex blood vessel structure of the retina. In this study, a hybrid algorithm is proposed using binarization, exclusively for segmenting the vessels from a retina image to enhance the exactness and specificity of segmentation of an image. The proposed algorithm extracts the advantages of pattern recognition techniques, such as Matched Filter (MF), Matched Filter with First-order Derivation of Gaussian (MF-FDOG), Multi-Scale Line Detector (MSLD) algorithms and developed as a hybrid algorithm. This algorithm is authenticated with the openly accessible dataset DRIVE. Using Python with OpenCV, the algorithm simulation results had attained an accurateness of 0.9602, a sensitivity of 0.6246, and a specificity of 0.9815 for the dataset. Simulation outcomes proved that the proposed hybrid algorithm accurately segments the blood vessels of the retina compared to the existing methodologies.

Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation.

Retinal vessel segmentation is essential for the detection and diagnosis of eye diseases. However, it is difficult to accurately identify the vessel boundary due to the large variations of scale in the retinal vessels and the low contrast between the vessel and the background. Deep learning has a good effect on retinal vessel segmentation since it can capture representative and distinguishing features for retinal vessels. An improved U-Net algorithm for retinal vessel segmentation is proposed in this paper. To better identify vessel boundaries, the traditional convolutional operation CNN is replaced by a global convolutional network and boundary refinement in the coding part. To better divide the blood vessel and background, the improved position attention module and channel attention module are introduced in the jumping connection part. Multiscale input and multiscale dense feature pyramid cascade modules are used to better obtain feature information. In the decoding part, convolutional long and short memory networks and deep dilated convolution are used to extract features. In public datasets, DRIVE and CHASE_DB1, the accuracy reached 96.99% and 97.51%. The average performance of the proposed algorithm is better than that of existing algorithms.

A modified Coye algorithm for retinal vessel segmentation

A modified Coye algorithm for retinal vessel segmentation

Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network.

Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90 and 96.88%, and the average specificity is 98.85 and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.

Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease.

UNet retinal blood vessel segmentation algorithm based on improved pyramid pooling method and attention mechanism

The segmentation results of retinal vessels have a significant impact on the automatic diagnosis of retinal diabetes, hypertension, cardiovascular and cerebrovascular diseases and other ophthalmic diseases. In order to improve the performance of blood vessels segmentation, a pyramid scene parseing U-Net segmentation algorithm based on attention mechanism was proposed. The modified PSP-Net pyramid pooling module is introduced on the basis of U-Net network, which aggregates the context information of different regions so as to improve the ability of obtaining global information. At the same time, attention mechanism was introduced in the skip connection part of U-Net network, which makes the integration of low-level features and high-level semantic features more efficient and reduces the loss of feature information through nonlinear connection mode. The sensitivity, specificity, accuracy and AUC of DRIVE and CHASE_DB1 data sets are 0.7814, 0.9810, 0.9556, 0.9780; 0.8195, 0.9727, 0.9590, 0.9784. Experimental results show that the PSP-UNet segmentation algorithm based on the attention mechanism enhances the detection ability of blood vessel pixels, suppresses the interference of irrelevant information and improves the network segmentation performance, which is superior to U-Net algorithm and some mainstream retinal vascular segmentation algorithms at present.

Densely connected U-Net retinal vessel segmentation algorithm based on multi-scale feature convolution extraction.

The segmentation results of retinal blood vessels have a significant impact on the automatic diagnosis of various ophthalmic diseases. In order to further improve the segmentation accuracy of retinal vessels, we propose an improved algorithm based on multiscale vessel detection, which extracts features through densely connected networks and reuses features. A parallel fusion and serial embedding multiscale feature dense connection U-Net structure are designed. In the parallel fusion method, features of the input images are extracted for Inception multiscale convolution and dense block convolution, respectively, and then the features are fused and input into the subsequent network. In serial embedding mode, the Inception multiscale convolution structure is embedded in the dense connection network module, and then the dense connection structure is used to replace the classical convolution block in the U-Net network encoder part, so as to achieve multiscale feature extraction and efficient utilization of complex structure vessels and thereby improve the network segmentation performance. The experimental analysis on the standard DRIVE and CHASE_DB1 databases shows that the sensitivity, specificity, accuracy, and AUC of the parallel fusion and serial embedding methods reach 0.7854, 0.9813, 0.9563, 0.9794; 0.7876, 0.9811, 0.9565, 0.9793 and 0.8110, 0.9737, 0.9547, 0.9667; 0.8113, 0.9717, 0.9574, 0.9750, respectively. The experimental results show that multiscale feature detection and feature dense connection can effectively enhance the network model's ability to detect blood vessels and improve the network segmentation performance, which is superior to U-Netalgorithm and some mainstream retinal blood vessel segmentation algorithms at present.

Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network.

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.

Assessing fairness in performance evaluation of publicly available retinal blood vessel segmentation algorithms

In the literature, various algorithms have been proposed for automatically extracting blood vessels from retinal images. In general, they are developed and evaluated using several publicly available datasets such as the DRIVE and STARE datasets. For performance evaluation, several metrics such as Sensitivity, Specificity, and Accuracy have been widely used. However, not all methods in the literature have been fairly evaluated and compared among their counterparts. In particular, for some publicly available algorithms, the performance is measured only for the area inside the field of view (FOV) of each retinal image while the rest use the complete image for the performance evaluation. Therefore, performing a comparison of the performance of methods in the latter group with those in the former group may lead to a misleading justification. This study aims to assess fairness in the performance evaluation of various publicly available retinal blood vessel segmentation algorithms. The conducted study allows getting several meaningful results: (i) a guideline to assess fairness in performance evaluation of retinal vessel segmentation algorithms, (ii) a more proper performance comparison of retinal vessel segmentation algorithms in the literature, and (iii) a suggestion regarding the use of performance evaluation metrics that will not lead to misleading comparison and justification.

Retinal Vessel Segmentation based on Convolutional Neural Network and Connection Domain Detection

We propose a retinal vessel segmentation algorithm based on a convolution neural network and connected domain detection. After defining the discriminant matrix, we constructed and trained a convolutional neural network model, which can realize the mapping relationship from eye fundus grayscale to the discriminant matrix. This model achieves the preliminary segmentation of retinal vessels. The prediction of uncertain pixels is revised by using the geometric characteristics of the vessels and through the analysis of connected regions. The experimental results show good generalization ability, the average segmentation accuracy, specificity, and sensitivity are 96.64%, 97.96%, and 80.68%, respectively.

Weakly supervised retinal vessel segmentation algorithm without groundtruth

In the current image processing field, medical image segmentation needs a lot of groundtruths, and the process of making these groundtruths is time-consuming and laborious. Thus, a novel retinal vessel segmentation algorithm without groundtruth is proposed in this Letter. The hierarchical clustering algorithm is first used to binary classify vessel and non-vessel pixels. Then classification results based on DRIVE databases are used as pseudo groundtruths to train the neural networks and transfer learning is considered for subsequent processing. Next the trained network is used as the feature extraction tool, by calculating and comparing the difference of image features between the target domain data (DRIVE database) and the source domain data (STARE, CHASE DB1, and HRF databases) extracted from the network. The data required for training is expanded based on semi-supervised clustering in this image feature space, finally the deep neural network is further fine-tuned. Experiments on the publicly available fundus image dataset DRIVE demonstrate that the proposed method outperforms many other state-of-the-art weakly supervised and unsupervised methods.