Retinal Blood Vessel Morphology Research Articles

Optometry in adults with microdeletion 22q11.2: The eye as a window to the brain

PurposeThe 22q11.2 deletion syndrome (22q11.2DS) has an estimated prevalence of 1:2148 live births and is associated with an increased risk of schizophrenia, cognitive decline and early-onset Parkinson’s disease. Because retinal and cerebral tissue share embryological, physiological and anatomical characteristics, retinal blood vessel morphology and retinal nerve fiber layer (RNFL) thickness have been proposed as non-invasive biomarkers for psychiatric and neurodegenerative disorders. In this exploratory study, we examined these potential biomarkers in adults with 22q11.2DS relative to controls, and in relation to age. MethodsCentral retinal artery and vein equivalent, fractal dimension, and vascular tortuosity, obtained through fundoscopy, and peripapillary RNFL and macular thickness, obtained through optical coherence tomography, were compared between adults with 22q11.2DS and sex- and age-matched controls. ResultsMean retinal vascular fractal dimension and tortuosity values were significantly higher in the group of adults with 22q11.2DS than in controls (p < 0.001; p < 0.001). RNFL was thicker in the nasal segment (p = 0.002) in 22q11.2DS, and a trend for thinner RNFL in the nasal and temporal inferior segments (p = 0.05 and p = 0.06, respectively) was found. There were significant negative correlations with age for fractal dimension (p < 0.001) and RNFL thickness in the global (p = 0.007), temporal inferior (p = 0.005) and temporal superior (p = 0.04) segments in adults with 22q11.2DS, but not in controls. ConclusionsOur results indicate higher retinal vascular fractal dimension and tortuosity, and a decrease in fractal dimension and RNFL thickness in relation to age in adults with 22q11.2DS. Our findings support future studies that focus on retinal fractal dimension and RNFL thickness as potential biomarkers for age-related manifestations in 22q11.2 including psychotic and (early) neurodegenerative disorders.

ResDO-UNet: A deep residual network for accurate retinal vessel segmentation from fundus images

For the clinical diagnosis, it is essential to obtain accurate morphology data of retinal blood vessels from patients, and the morphology of retinal blood vessels can well help doctors to judge the patient’s condition and give targeted therapeutic measures. Conventional manual retinal blood vessel segmentation by the doctors from the fundus images is time-consuming and laborious, while it also requires the rich doctor’s expertise. With the strong context feature expression ability of deep convolutional neural networks (DCNN), it has shown a promising performance on retinal blood vessel segmentation, specially U-shape network (U-Net) and its variant. However, due to the information loss issue caused by multiple pooling operations and insufficient process issue of local context features by skip connections, most of segmentation methods still exist a certain shortcoming on accurate fine vessel detection. To address this issue, based on the encoder–decoder framework, a novel retinal vessel segmentation network, called ResDO-UNet, is proposed to provide an automatic and end-to-end detection scheme from fundus images. To enhance feature extraction capabilities, combined with depth-wise over-parameterized convolutional layer (DO-conv), a residual DO-conv (ResDO-conv) network is proposed to act as the backbone network to acquire strong context features. In addition, to reduce the effect of information loss caused by multiple pooling operations, taking advantages of max pooling and average pooling layers, a pooling fusion block (PFB) is proposed to realize nonlinear fusion pooling. Meanwhile, faced with insufficient process of local context features by skip connections, an attention fusion block (AFB) is proposed to realize effective multi-scale feature expression. Combined with the three public available data sets on retinal vessel segmentation, including DRIVE, STARE and CHASE_DB1, the proposed segmentation network could reach a state-of-the-art detection performance compared to other related advanced work.

A Case of Normal Retinal Blood Vessel Pattern Using Fundus Camera

The purpose of this study is to report the specific pattern of retinal blood vessel morphology in subjects with normal retina. The fundus of a 22-year-old female subject with normal ophthalmic history and visual acuity was tested. In optical coherence tomography, retinal nerve fiver layer thickness and C/D ratio were normal. As a result of confirming the distribution of retinal blood vessels using a fundus camera, there were no specific findings in the optic nerve papilla originating from the central retinal artery. But it was observed that the central retinal artery in the right eye divided into upper and lower branches after toward the ear. There may be retinal blood vessels travel that is not included in the 10 modalities that classify the morphology of the central retinal artery. Therefore, it is considered that additional confirmation is necessary.

SUD-GAN: Deep Convolution Generative Adversarial Network Combined with Short Connection and Dense Block for Retinal Vessel Segmentation.

Since morphology of retinal blood vessels plays a key role in ophthalmological disease diagnosis, retinal vessel segmentation is an indispensable step for the screening and diagnosis of retinal diseases with fundus images. In this paper, deep convolution adversarial network combined with short connection and dense block is proposed to separate blood vessels from fundus image, named SUD-GAN. The generator adopts U-shape encode-decode structure and adds short connection block between convolution layers to prevent gradient dispersion caused by deep convolution network. The discriminator is all composed of convolution block, and dense connection structure is added to the middle part of the convolution network to strengthen the spread of features and enhance the network discrimination ability. The proposed method is evaluated on two publicly available databases, the DRIVE and STARE. The results show that the proposed method outperforms the state-of-the-art performance in sensitivity and specificity, which were 0.8340 and 0.9820, and 0.8334 and 0.9897 respectively on DRIVE and STARE, and can detect more tiny vessels and locate the edge of blood vessels more accurately.

Image processing-based model for tortuosity measurement of retinal blood vessels

Purpose: The study of retinal blood vessel morphology is of great importance in retinal image analysis. The retinal blood vessels have a number of distinct features such as width, diameter, tortuosity, etc. In this paper, a method is proposed to measure the tortuosity of retinal blood vessels obtained from retinal fundus images. Tortuosity is a situation in which blood vessels become tortuous, that is, curved or non-smooth. It is one of the earliest changes that occur in blood vessels in some retinal diseases. Hence, its detection at an early stage can prevent the progression of retinal diseases such as diabetic retinopathy, hypertensive retinopathy, retinopathy of prematurity, etc. The present study focuses on the measurement of retinal blood vessel tortuosity for the analysis of hypertensive retinopathy. Hypertensive retinopathy is a condition in which the retinal vessels undergo changes and become tortuous due to long term high blood pressure. Early recognition of hypertensive retinopathy signs remains an important step in determining the target-organ damage and risk assessment of hypertensive patients. Hence, this paper attempts to estimate tortuosity using image-processing techniques that have been tested on artery and vein segments of retinal images.&#x0D; Design: Image processing-based model designed to measure blood vessel tortuosity.&#x0D; Methods: In this paper, a novel image processing-based model is proposed for tortuosity measurement. This parameter will be helpful for analyzing hypertensive retinopathy. To test the eff ectiveness of the system in determining tortuosity, the method is first applied on a set of synthetically generated blood vessels. Then, the method is repeated on blood vessel (both artery and vein) segments extracted from retinal images collected from publicly available databases and on images collected from a local eye hospital. The blood vessel segment images that are used in the method are binary images where blood vessels are represented by white pixels (foreground), while black pixels represent the background. Vessels are then classified into normal, moderately tortuous, and severely tortuous by following the analysis performed on the images in the Retinal Vessel Tortuosity Data Set (RET-TORT) obtained from BioIm Lab, Laboratory of Biomedical Imaging (Padova, Italy). This database consists of 30 artery segments and 30 vein segments, which were manually ordered on the basis of increasing tortuosity by Dr. S. Piermarocchi, a retinal specialist belonging to the Department of Ophthalmology of the University of Padova (Italy). The artery and vein segments with the fewest number of turns are given a low tortuosity ranking, while those with the greatest number of turns are given a high tortuosity ranking by the expert. Based on this concept, our proposed method defines retinal image segments as normal when they present the fewest number of twists/turns, moderately tortuous when they present more twists/turns than normal but fewer than severely tortuous vessels, and severely tortuous when they present a greater number of twists/turns than moderately tortuous vessels. On implementing our image processing-based method on binary blood vessel segment images that are represented by white pixels, it is found that the vessel pixel (white pixels) count increases with increasing vessel tortuosity. That is, for normal blood vessels, the white pixel count is less compared to moderately tortuous and severely tortuous vessels. It should be noted that the images obtained from the different databases and from the local hospital for this experiment are not hypertensive retinopathy images. Instead, they are mostly normal eye images and very few of them show tortuous blood vessels.&#x0D; Results: The results of the synthetically generated vessel segment images from the baseline for the evaluation of retinal blood vessel tortuosity. The proposed method is then applied on the retinal vessel segments that are obtained from the DRIVE and HRF databases, respectively. Finally, to evaluate the capability of the proposed method in determining the tortuosity level of the blood vessels, the method is tested with a standard tortuous database, namely, the RET-TORT database. The results are then compared with the tortuosity level mentioned in the database. It was found that our method is able to classify blood vessel images as normal, moderately tortuous, and severely tortuous, with results closely matching the clinical ordering provided by the expert in the RET-TORT database. Subjective evaluation was also performed by research scholars and postgraduate students to cross-validate the results.&#x0D; Conclusion: The close correlation between the tortuosity evaluation using the proposed method and the clinical ordering of retinal vessels as provided by the retinal specialist in the RET-TORT database shows that, although simple, this method can evaluate the tortuosity of vessel segments effectively.&#x0D;

Influencing factors affecting the retinal blood vessel morphology in patients with diabetes mellitus

Influencing factors affecting the retinal blood vessel morphology in patients with diabetes mellitus

Commentary: "prom1 function in development, intestinal inflammation, and intestinal tumorigenesis".

Commentary: "prom1 function in development, intestinal inflammation, and intestinal tumorigenesis".

Retinal vessel segmentation employing ANN technique by Gabor and moment invariants-based features

Diabetic retinopathy (DR) is the major ophthalmic pathological cause for loss of eye sight due to changes in blood vessel structure. The retinal blood vessel morphology helps to identify the successive stages of a number of sight threatening diseases and thereby paves a way to classify its severity. This paper presents an automated retinal vessel segmentation technique using neural network, which can be used in computer analysis of retinal images, e.g., in automated screening for diabetic retinopathy. Furthermore, the algorithm proposed in this paper can be used for the analysis of vascular structures of the human retina. Changes in retinal vasculature are one of the main symptoms of diseases like hypertension and diabetes mellitus. Since the size of typical retinal vessel is only a few pixels wide, it is critical to obtain precise measurements of vascular width using automated retinal image analysis. This method segments each image pixel as vessel or nonvessel, which in turn, used for automatic recognition of the vasculature in retinal images. Retinal blood vessels are identified by means of a multilayer perceptron neural network, for which the inputs are derived from the Gabor and moment invariants-based features. Back propagation algorithm, which provides an efficient technique to change the weights in a feed forward network is utilized in our method. The performance of our technique is evaluated and tested on publicly available DRIVE database and we have obtained illustrative vessel segmentation results for those images.

Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images

Diabetic retinopathy is a severe sight threatening disease which causes blindness among working age people. This research work presents a retinal vessel segmentation technique, which can be used in computer based retinal image analysis. This proposed method could be used as a prescreening system for the early detection of diabetic retinopathy. The algorithm implemented in this work can be effectively used for detection and analysis of vascular structures in retinal images. The retinal blood vessel morphology helps to classify the severity and identify the successive stages of a number of diseases. The changes in retinal vessel diameter are one of the symptoms for diseases based on vascular pathology. The size of typical retinal vessel is a few pixels wide and it becomes critical and challenging to obtain precise measurements using computer based automatic analysis of retinal images. This method classifies each image pixel as vessel or non-vessel and thereby produces the segmentation of vasculature in retinal images. Retinal blood vessels are identified and segmented by making use of a multilayer perceptron neural network, for which the inputs are derived from three primary colour components of the image, i.e., red, green and blue. Back propagation algorithm which provides a proficient technique to change the weights in a feed-forward network is employed. The performance of this method was evaluated and tested using the retinal images from the DRIVE database and has obtained illustrative results. The measured accuracy of the proposed system was 95.03% for the segmentation algorithm tested on this database.

Simultaneously Identifying All True Vessels From Segmented Retinal Images

Measurements of retinal blood vessel morphology have been shown to be related to the risk of cardiovascular diseases. The wrong identification of vessels may result in a large variation of these measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of automatically identifying true vessels as a postprocessing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to solve this optimization problem and evaluate it on a large real-world dataset of 2,446 retinal images. Experiment results are analyzed with respect to actual measurements of vessel morphology. The results show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.

The detection and quantification of retinopathy using digital angiograms

An algorithm is presented for the analysis and quantification of the vascular structures of the human retina. Information about retinal blood vessel morphology is used in grading the severity and progression of a number of diseases. These disease processes are typically followed over relatively long time courses, and subjective analysis of the sequential images dictates the appropriate therapy for these patients. In this research, retinal fluorescein angiograms are acquired digitally in a 1024x1024 16-b image format and are processed using an automated vessel tracking program to identify and quantitate stenotic and/or tortuous vessel segments. The algorithm relies on a matched filtering approach coupled with a priori knowledge about retinal vessel properties to automatically detect the vessel boundaries, track the midline of the vessel, and extract useful parameters of clinical interest. By modeling the vessel profile using Gaussian functions, improved estimates of vessel diameters are obtained over previous algorithms. An adaptive densitometric tracking technique based on local neighborhood information is also used to improve computational performance in regions where the vessel is relatively straight.