Blood Vessel Extraction Research Articles

Skeleton-guided multi-scale dual-coordinate attention aggregation network for retinal blood vessel segmentation

Deep learning plays a pivotal role in retinal blood vessel segmentation for medical diagnosis. Despite their significant efficacy, these techniques face two major challenges. Firstly, they often neglect the severe class imbalance in fundus images, where thin vessels in the foreground are proportionally minimal. Secondly, they are susceptible to poor image quality and blurred vessel edges, resulting in discontinuities or breaks in vascular structures. In response, this paper proposes the Skeleton-guided Multi-scale Dual-coordinate Attention Aggregation (SMDAA) network for retinal vessel segmentation. SMDAA comprises three innovative modules: Dual-coordinate Attention (DCA), Unbalanced Pixel Amplifier (UPA), and Vessel Skeleton Guidance (VSG). DCA, integrating Multi-scale Coordinate Feature Aggregation (MCFA) and Scale Coordinate Attention Decoding (SCAD), meticulously analyzes vessel structures across various scales, adept at capturing intricate details, thereby significantly enhancing segmentation accuracy. To address class imbalance, we introduce UPA, dynamically allocating more attention to misclassified pixels, ensuring precise extraction of thin and small blood vessels. Moreover, to preserve vessel structure continuity, we integrate vessel anatomy and develop the VSG module to connect fragmented vessel segments. Additionally, a Feature-level Contrast (FCL) loss is introduced to capture subtle nuances within the same category, enhancing the fidelity of retinal blood vessel segmentation. Extensive experiments on three public datasets (DRIVE, STARE, and CHASE_DB1) demonstrate superior performance in comparison to current methods. The code is available at https://github.com/wangwxr/SMDAA_NET.

Soft Attention Mechanism Based Network to Extract Blood Vessels From Retinal Image Modality

Soft Attention Mechanism Based Network to Extract Blood Vessels From Retinal Image Modality

Skip connection information enhancement network for retinal vessel segmentation.

Many major diseases of the retina often show symptoms of lesions in the fundus of the eye. The extraction of blood vessels from retinal fundus images is essential to assist doctors. Some of the existing methods do not fully extract the detailed features of retinal images or lose some information, making it difficult to accurately segment capillaries located at the edges of the images. In this paper, we propose a multi-scale retinal vessel segmentation network (SCIE_Net) based on skip connection information enhancement. Firstly, the network processes retinal images at multiple scales to achieve network capture of features at different scales. Secondly, the feature aggregation module is proposed to aggregate the rich information of the shallow network. Further, the skip connection information enhancement module is proposed to take into account the detailed features of the shallow layer and the advanced features of the deeper network to avoid the problem of incomplete information interaction between the layers of the network. Finally, SCIE_Net achieves better vessel segmentation performance and results on the publicly available retinal image standard datasets DRIVE, CHASE_DB1, and STARE.

Interval type-2 fuzzy approach for retinopathy detection in fundus images

In the manuscript, an automatic approach for analysis and detection of various stages of retinopathy defects in human eyes has been proposed. The approach consists of a robust preprocessing technique of the retina fundus image to mitigate the effects of noise and poor lightening in the image. To realize a compressive analysis of the defects, methods for extracting blood vessels and optic disc in the fundus image has also been developed. Adaptive Histogram Equalization (AHE), median filtering and Connected Component Analysis techniques were used in separating blood vessels and optic disc from each fundus image. The pre-processing utilizes canny edge detection and Morphological Closing on the fundus image. An interval type-2 fuzzy (IT2F) clustering is applied to segments an input image into four clusters. These four clusters from the fuzzy segmentation are further analyzed to extract various stages of retinopathy abnormalities (e.g., Hemorrhage, hard exudates etc.). The extracted blood vessels and optic disc are removed from the analysis to enhance the defects detection process. Experiments were conducted on DIARETDB1 database. The experimental results obtained are validated using the ground-truth images contained in DIARETDB1 database. Impressive results are recorded throughout the experiment. Hard-Exudates and Hemorrhage were detected from the fundus images and results from similarity indexes such as, accuracy (94.11%) sensitivity (93.03%) and specificity (98.45%) were recorded.

Automated extraction of blood vessels in retinal images using rolling guidance filter

Automated extraction of blood vessels in retinal images using rolling guidance filter

Screening Retinal Images and Extraction of the Retinal Blood Vessel for Identifying Diseases and Classification of Arteries and Veins by Using Deep Learning

Abstract: In recent years, the extraction of retinal blood vessels from low contrast retinal images has become a challenging task for diagnosing retinal diseases such as Diabetic Retinopathy, Agerelated Macular Degeneration (AMD), Retinopathy of Prematurity (ROP), cataract, and glaucoma. Another challenge is screening the retinal image to identify the disease early on. However, data analysis from a large population-based study of retinal diseases is required to help resolve the uncertainty in identifying the retinal disease based on retinal image classification using deep learning approaches from the retinal diseases dataset. Therefore, we proposed the survey on the deep learning approach for screening the retinal image to identify the early stages of the disease and discussed retinal disease analysis based on deep learning approaches to detect Diabetic Retinopathy, AMD ROP, and Glaucoma. We also discuss deep learning applications in the segmentation of retinal blood vessels, extraction of the optic disc, optic cup, and fovea, and OCT segmentation to detect retinal disease for diagnosis of diseases. Finally, discuss the classification of arteries/veins using a deep learning approach.

ESDiff: a joint model for low-quality retinal image enhancement and vessel segmentation using a diffusion model.

In clinical screening, accurate diagnosis of various diseases relies on the extraction of blood vessels from fundus images. However, clinical fundus images often suffer from uneven illumination, blur, and artifacts caused by equipment or environmental factors. In this paper, we propose a unified framework called ESDiff to address these challenges by integrating retinal image enhancement and vessel segmentation. Specifically, we introduce a novel diffusion model-based framework for image enhancement, incorporating mask refinement as an auxiliary task via a vessel mask-aware diffusion model. Furthermore, we utilize low-quality retinal fundus images and their corresponding illumination maps as inputs to the modified UNet to obtain degradation factors that effectively preserve pathological features and pertinent information. This approach enhances the intermediate results within the iterative process of the diffusion model. Extensive experiments on publicly available fundus retinal datasets (i.e. DRIVE, STARE, CHASE_DB1 and EyeQ) demonstrate the effectiveness of ESDiff compared to state-of-the-art methods.

Extraction of Retinal Blood Vessels in Colour Retinal Images: An Analysis

Extraction of Retinal Blood Vessels in Colour Retinal Images: An Analysis

RETINAL VESSELS EXTRACTION BASED ON IMPROVING LINE DETECTION

In the medical field, medical images have supported doctors in detecting some dangerous problems related to health. These problems are caused by abnormalities in retinal blood vessels. Therefore, analysis of retinal vascular features is a good way to detect these abnormalities that help doctors make suitable treatment plans in the early stage. One of the retinal analysis tasks is retinal blood vessel extraction which plays an important role because it needs to be done before any measurement can be created. This paper proposed an approach for retinal blood vessel extraction based on multi-scale line detection. In the proposed method, the input image is applied by a multi-scale line detection technique to detect retinal blood vessels. As a result, our method can work effectively to extract more vessels. The performance of the proposed method evaluates both quantitatively and qualitatively on publicly available DRIVE datasets with an accuracy average reaching 96%. The result of the proposed method is better than the other methods.

Biometric System for Person Authentication Using Retinal Vascular Branching Pattern

The person’s retina has its uniqueness that can be used as biometric recognition. The use of the retina as a marking feature in biometrics is more accurate in making calls, verification, and authentication. Retinal biometric characteristics are unique and difficult to manipulate, thus making the retinal biometric system one of the most reliable biometrics compared to other biometric characteristics. The retinal biometric system can be formed using extracted retinal vessels. The difficulty in extracting retinal vessels is a characteristic of retinal vessels. itself includes (central artery, central branch artery, central vein and central branch vein), the ratio of the thickness ratio between the different retinal arteries and veins (2:3), the location of the retinal artery and vein and the color. This complexity often results in errors in the retinal blood vessel extraction process, where not all blood vessel objects can be extracted properly which can reduce the accuracy of the retinal biometric system. This study will address the problem of extracting retinal vessels by proposing the use of an extraction method to produce truly unique retinal features to be included in the retinal biometric system by tracing all branches of the retinal vessels (consisting of: bifurcation, trifurcation and crossover). ). The accuracy results show that 99.81% of the images were correctly detected. The blood pattern is obtained by doing extraction which includes the preprocessing stage and is continued by doing the blood extraction stage. This pattern extraction result is used as a unique pattern to be included in the feature vector of the biometric system in identifying person based on the retina.

Intracranial steno-occlusive lesion detection on time-of-flight MR angiography using multi-task learning.

Steno-occlusive lesions in intracranial arteries refer to segments of narrowed or occluded blood vessels that increase the risk of ischemic strokes. Steno-occlusive lesion detection is crucial in clinical settings; however, automatic detection methods have hardly been studied. Therefore, we propose a novel automatic method to detect steno-occlusive lesions in sequential transverse slices on time-of-flight magnetic resonance angiography. Our method simultaneously detects lesions while segmenting blood vessels based on end-to-end multi-task learning, reflecting that the lesions are closely related to the connectivity of blood vessels. We design classification and localization modules that can be attached to arbitrary segmentation network. As blood vessels are segmented, both modules simultaneously predict the presence and location of lesions for each transverse slice. By combining outputs from the two modules, we devise a simple operation that boosts the performance of lesion localization. Experimental results show that lesion prediction and localization performance is improved by incorporating blood vessel extraction. Our ablation study demonstrates that the proposed operation enhances lesion localization accuracy. We also verify the effectiveness of multi-task learning by comparing our approach with those that individually detect lesions with extracted blood vessels.

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.

LEA U-Net: a U-Net-based deep learning framework with local feature enhancement and attention for retinal vessel segmentation

Feature extraction of the retinal blood vessel is one of the crucial tasks in the prediction of ophthalmologic diseases. Important features are extracted based on image segmentation results. The efficiency of vessel segmentation methods could help doctors in the diagnostic of several relevant diseases as early as possible. Recently, U-Net has achieved good results in many medical image segmentation tasks, especially for images of blood vessels. However, due to the limitation of the network structure, some small features could be lost in the transmission process. As a result, there are still many research gaps for U-Net-based retinal vessel segmentation works. In this paper, we propose an improved U-Net based model to segment images of retinal vessels. The improvement focuses on U-Net from two aspects: designing a local feature enhancement module composed of dilated convolution and 1times 1 convolution to enhance the feature extraction of tiny vessels; integrating an attention mechanism with skip connection of the network to highlight features related to vessel segmentation information taken from the down-sampling part to the up-sampling part. The performance of the proposed model was evaluated and compared with several published state-of-the-art approaches on the same public dataset—DRIVE, and the proposed method achieved an accuracy of 0.9563, F1-score of 0.823, TPR of 0.7983, and TNR of 0.9793. The AUC of PRC is 0.9109 and the AUC of ROC is 0.9794. The results proved the potential for clinical applications.

Identification of Mutated in colorectal cancer (MCC) protein as a novel partner of PDZRN3 during Blood brain barrier development

Blood brain barrier (BBB) disruption is a critical component of the physiopathology of neurological disorder. Wnt signalling is crucial for BBB development and its stability. We have reported that an endothelial excessive activation of the ubiquitin ligase PDZRN3, a partner in Wnt signalling, destabilizes the BBB. However, the molecular pathway is still poorly understood. Using a differential screening strategy by BioID, MCC was discovered as a potential PDZRN3 interacting partner. This study focused on understanding how MCC/PDZRN3 interaction regulates Wnt signalling in vitro and in vivo. We then investigated the role of MCC in endothelial function. PDZRN3 and MCC protein partners were identified by a proteomic screening strategy (bioID). MCC functional role in Wnt signalling was studied in brain endothelial cells (HBMEC) using siRNA strategy, and specific chemical inhibitors. MCC in vivo expression was analysed in extracted blood vessels from wild type and transgenic mouse brains. MCC is a direct target of the kinase CKIɛ, a key regulator of the canonical Wnt pathway. We demonstrated that MCC is constantly phosphorylated by this kinase. We then reported that PDZRN3 prevents CKIɛ-induced phosphorylation of MCC. During post-natal mouse BBB development, MCC undergoes a switch from un- to hyper-phosphorylated state, correlated with a decrease in PDZRN3 expression level. Interestingly, in mouse mutants, specific ectopic Pdzrn3 over-expression in brain endothelial cells impairs MCC phosphorylation switch. We then investigated MCC involvement in endothelial function. MCC knock down impairs HBMEC directed migration under flow condition and planar MTOC polarization. Finally, using a bioID screening, we have identified several centrosomal proteins, such as CEP131, as potential MCC partners. Centrosome being a key organelle in cell polarization, we propose that MCC may regulate its organisation during HBMEC planar migration. By forming a stable complex with MCC, PDZRN3 prevents CKIɛ-induced phosphorylation of MCC. MCC would then regulate centrosome polarisation required for endothelial planar migration, making MCC an actor of BBB dynamic maturation.

TUnet-LBF: Retinal fundus image fine segmentation model based on transformer Unet network and LBF

Segmentation of retinal fundus images is a crucial part of medical diagnosis. Automatic extraction of blood vessels in low-quality retinal images remains a challenging problem. In this paper, we propose a novel two-stage model combining Transformer Unet (TUnet) and local binary energy function model (LBF), TUnet-LBF, for coarse to fine segmentation of retinal vessels. In the coarse segmentation stage, the global topological information of blood vessels is obtained by TUnet. The neural network outputs the initial contour and the probability maps, which are input to the fine segmentation stage as the priori information. In the fine segmentation stage, an energy modulated LBF model is proposed to obtain the local detail information of blood vessels. The proposed model reaches accuracy (Acc) of 0.9650, 0.9681 and 0.9708 on the public datasets DRIVE, STARE and CHASE_DB1 respectively. The experimental results demonstrate the effectiveness of each component in the proposed model.

Retinal Blood-Vessel Extraction Using Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Functions.

Automated blood-vessel extraction is essential in diagnosing Diabetic Retinopathy (DR) and other eye-related diseases. However, the traditional methods for extracting blood vessels tend to provide low accuracy when dealing with difficult situations, such as extracting both micro and large blood vessels simultaneously with low-intensity images and blood vessels with DR. This paper proposes a complete preprocessing method to enhance original retinal images before transferring the enhanced images to a novel blood-vessel extraction method by a combined three extraction stages. The first stage focuses on the fast extraction of retinal blood vessels using Weighted Kernel Fuzzy C-Means (WKFCM) Clustering to draw the vessel feature from the retinal background. The second stage focuses on the accuracy of full-size images to achieve regional vessel feature recognition of large and micro blood vessels and to minimize false extraction. This stage implements the mathematical dilation operator from a trained model called Dilation-Based Function (DBF). Finally, an optimal parameter threshold is empirically determined in the third stage to remove non-vessel features in the binary image and improve the overall vessel extraction results. According to evaluations of the method via the datasets DRIVE, STARE, and DiaretDB0, the proposed WKFCM-DBF method achieved sensitivities, specificities, and accuracy performances of 98.12%, 98.20%, and 98.16%, 98.42%, 98.80%, and 98.51%, and 98.89%, 98.10%, and 98.09%, respectively.

Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments.

Hemodynamic prediction of carotid artery stenosis (CAS) is of great clinical significance in the diagnosis, prevention, and treatment prognosis of ischemic strokes. While computational fluid dynamics (CFD) is recognized as a useful tool, it shows a crucial issue that the high computational costs are usually required for real-time simulations of complex blood flows. Given the powerful feature-extraction capabilities, the deep learning (DL) methodology has a high potential to implement the mapping of anatomic geometries and CFD-driven flow fields, which enables accomplishing fast and accurate hemodynamic prediction for clinical applications. Based on a brain/neck CT angiography database of 280 subjects, image based three-dimensional CFD models of CAS were constructed through blood vessel extraction, computational domain meshing and setting of the pulsatile flow boundary conditions; a series of CFD simulations were undertaken. A DL strategy was proposed and accomplished in terms of point cloud datasets and a DL network with dual sampling-analysis channels. This enables multimode mapping to construct the image-based geometries of CAS while predicting CFD-based hemodynamics based on training and testing datasets. The CFD simulation was validated with the mass flow rates at two outlets reasonably agreed with the published results. Comprehensive analysis and error evaluation revealed that the DL strategy enables uncovering the association between transient blood flow characteristics and artery cavity geometric information before and after surgical treatments of CAS. Compared with other methods, our DL-based model trained with more clinical data can reduce the computational cost by 7,200 times, while still demonstrating good accuracy (error<12.5%) and flow visualization in predicting the two hemodynamic parameters. In addition, the DL-based predictions were in good agreement with CFD simulations in terms of mean velocity in the stenotic region for both the preoperative and postoperative datasets. This study points to the capability and significance of the DL-based fast and accurate hemodynamic prediction of preoperative and postoperative CAS. For accomplishing real-time monitoring of surgical treatments, further improvements in the prediction accuracy and flexibility may be conducted by utilizing larger datasets with specific real surgical events such as stent intervention, adopting personalized boundary conditions, and optimizing the DL network.

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

In recent years, there has been a significant increase in the number of people suffering from eye illnesses, which should be treated as soon as possible in order to avoid blindness. Retinal Fundus images are employed for this purpose, as well as for analysing eye abnormalities and diagnosing eye illnesses. Exudates can be recognised as bright lesions in fundus pictures, which can be the first indicator of diabetic retinopathy. With that in mind, the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis (IM-EDRD) with multi-level classifications. The model uses Support Vector Machine (SVM)-based classification to separate normal and abnormal fundus images at the first level. The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix (GLCM). Furthermore, the presence of Exudate and Diabetic Retinopathy (DR) in fundus images is detected using the Adaptive Neuro Fuzzy Inference System (ANFIS) classifier at the second level of classification. Exudate detection, blood vessel extraction, and Optic Disc (OD) detection are all processed to achieve suitable results. Furthermore, the second level processing comprises Morphological Component Analysis (MCA) based image enhancement and object segmentation processes, as well as feature extraction for training the ANFIS classifier, to reliably diagnose DR. Furthermore, the findings reveal that the proposed model surpasses existing models in terms of accuracy, time efficiency, and precision rate with the lowest possible error rate.

Retracted: Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm.

[This retracts the article DOI: 10.1155/2021/5573381.].

Automated extraction of blood vessels in retinal images using Rolling Guidance Filter (RGF)

Automated extraction of blood vessels in retinal images using Rolling Guidance Filter (RGF)