Tortuosity Of Retinal Blood Vessels Research Articles

A novel deep learning framework for the identification of tortuous vessels in plus diseased infant retinal images

Retinopathy of prematurity (ROP), sometimes known as Terry syndrome, is an ophthalmic condition that affects premature babies. It is the main cause of childhood blindness and morbidity of vision throughout life. ROP frequently coexists with a disease stage known as Plus disease, which is marked by severe tortuosity and dilated retinal blood vessels. The goal of this research is to create a diagnostic technique that can discriminate between infants with Plus disease from healthy subjects. Blood vascular tortuosity is used as a prognostic indicator for the diagnosis. We examine the quantification of retinal blood vessel tortuosity and propose a computer-aided diagnosis system that can be used as a tool for ROP identification. Deep neural networks are used in the proposed approach to segment retinal blood vessels, which is followed by the prediction of tortuous vessel pixels in the segmented vessel map. Digital fundus images obtained from Retcam3TM is used for screening. We use a proprietary data set of 289 infant retinal images (89 with Plus disease and 200 healthy) from Narayana Nethralaya in Bangalore, India, to illustrate the efficacy of our methodology. The findings of this study demonstrate the reliability of the proposed method as a computer-aided diagnostic tool that can help medical professionals make an early diagnosis of ROP.

A Comparison of the Tortuosity Phenomenon in Retinal Arteries and Veins Using Digital Image Processing and Statistical Methods

The tortuosity of retinal blood vessels is an important phenomenon, and it can act as a biomarker in the diagnosis of several eye diseases. The study of abnormalities in the tortuosity of retinal arteries and veins provides ophthalmologists with important information for disease diagnosis. Our study aims to compare the tortuosity relation between retinal arteries and veins by quantifying the vessels’ tortuosity in the retina using 14 tortuosity measures applied to the AV-classification retinal dataset. Two feature sets are created, one for arteries and the other for veins. The comparison between the tortuosity of arteries and veins is based on a two-sample T-test statistical method, a regression analysis between the quantified tortuosity features, principal component analysis at the dataset level, and the introduction of the arteriovenous length ratios concept to compare the variations in these new ratios to see the tortuosity behavior in each image. The methods’ results have shown that the tortuosity of retinal arteries and veins is similar. The result of the two-sample T-test supports the research hypothesis, as the P-value obtained was greater than 0.05. Furthermore, the regression analysis between arteries and veins features showed a high correlation (r2 = 89.39% and 89.11%) for arteries and veins, respectively. The study concludes that the retinal vessel type has no statistical significance in the tortuosity calculation results.

Retinal Vessel Local Tortuosity under a Macula-to-Optic Disc Central-Framing Change

Some ocular and cardiovascular diseases can be detected through the increased tortuosity of retinal blood vessels. Objective tortuosity measures can be obtained from digital image analysis of a retinography. This study tested a set of local tortuosity indices under a change in the frame center (macula, optic disc) of the eye fundus image. We illustrate the effects of such a change on 40 pairs of vessels evaluated with eight tortuosity indices. We show that the frame center change caused significant differences in the mean values of the vast majority of the tortuosity indices analyzed. The index defined as the ratio of the curvature to the arc length of a vessel segment proved to be the most robust in relation to a frame center change. Experimental results obtained from the analysis of clinical images are provided and discussed.

Sustained Upregulation of Endothelial Nox4 Mediates Retinal Vascular Pathology in Type 1 Diabetes.

NADPH oxidase 4 (Nox4) is a major source of reactive oxygen species (ROS) in retinal endothelial cells (ECs) and is upregulated under hyperglycemic and hypoxic conditions. However, the role of endothelial Nox4 upregulation in long-term retinal blood vessel damage in diabetic retinopathy (DR) remains undefined. Here, we attempted to address this question using humanized EC-specific Nox4 transgenic (hNox4EC-Tg) and EC-specific Nox4 knockout (Nox4EC-KO) mouse models. Our results show that hNox4EC-Tg mice at age of 10-12 months exhibited increased tortuosity of retinal blood vessels, focal vascular leakage, and acellular capillary formation. In vitro study revealed enhanced apoptosis in brain microvascular ECs derived from hNox4EC-Tg mice, concomitant with increased mitochondrial ROS, elevated lipid peroxidation, decreased mitochondrial membrane potential, and reduced mitochondrial respiratory function. In contrast, EC-specific deletion of Nox4 decreased mitochondrial ROS generation, alleviated mitochondrial damage, reduced EC apoptosis, and protected the retina from acellular capillary formation and vascular hyperpermeability in a streptozotocin-induced diabetes mouse model. These findings suggest that sustained upregulation of Nox4 in the endothelium contributes to retinal vascular pathology in diabetes, at least in part, through impairing mitochondrial function. Normalization of Nox4 expression in ECs may provide a new approach for prevention of vascular injury in DR.

Performance Analysis of Hybrid SOM and AdaBoost Classifiers for Diagnosis of Hypertensive Retinopathy

The diagnosis of hypertensive retinopathy (CAD-RH) can be made by observing the tortuosity of the retinal vessels. Tortuosity is a feature that is able to show the characteristics of normal or abnormal blood vessels. This study aims to analyze the performance of the CAD-RH system based on feature extraction tortuosity of retinal blood vessels. This study uses a segmentation method based on clustering self-organizing maps (SOM) combined with feature extraction, feature selection, and the ensemble Adaptive Boosting (AdaBoost) classification algorithm. Feature extraction was performed using fractal analysis with the box-counting method, lacunarity with the gliding box method, and invariant moment. Feature selection is done by using the information gain method, to rank all the features that are produced, furthermore, it is selected by referring to the gain value. The best system performance is generated in the number of clusters 2 with fractal dimension, lacunarity with box size 22-29, and invariant moment M1 and M3. Performance in these conditions is able to provide 84% sensitivity, 88% specificity, 7.0 likelihood ratio positive (LR+), and 86% area under the curve (AUC). This model is also better than a number of ensemble algorithms, such as bagging and random forest. Referring to these results, it can be concluded that the use of this model can be an alternative to CAD-RH, where the resulting performance is in a good category.

Artificial Intelligence for ROP Screening and to Assess Quality of Care: Progress and Challenges.

The goal of retinopathy of prematurity (ROP) screening is to detect the constellation of clinical signs that suggest a high risk of progression to retinal detachment so that urgent treatment can be given. At each screening episode, there are 3 considerations: whether urgent treatment is needed, whether follow-up screening is needed, and whether there is no risk of sight-threatening ROP so that the screening can stop. The decisions are based on a detailed examination of the retina focused on the severity (ie, stage), location (ie, zone), and degree of dilation and tortuosity of retinal blood vessels (ie, plus disease). If ROP is not present, the peripheral retina needs to be assessed to determine the degree of maturity of the retinal vessels; if they are mature or ROP detected earlier is definitely regressing, the screening can stop.Telemedicine with artificial intelligence (AI) image analysis could transform ROP screening, especially in settings with an insufficient supply of ophthalmologists, which can happen either because of absolute workforce shortages or when ophthalmologists choose not to offer ROP screening because of the time burden or fear of litigation. Telemedicine with AI image analysis is particularly appealing for low-resource settings in which greater exposure to risk factors, such as hyperoxia, can lead to a high frequency of treatment-requiring ROP in infants born at a gestational age (GA) of <32 weeks, and severe disease can also develop in more mature infants.1 In these settings, wider screening criteria are required, which increases the screening workload.2In this issue of Pediatrics, Campbell et al3 describe a multicenter screening program in India, the Retinopathy of Prematurity Eradication Save Our Sight program, which was done in collaboration with a United States–based international group, Imaging and Informatics in Retinopathy of Prematurity (iROP). This report describes not only the diagnostic-test accuracy of the deep learning (DL) system developed by this group (iROP DL) but, also, whether the findings can be used as an indicator to assess the quality of neonatal care.iROP DL, which quantifies plus disease, performed well, with 100% sensitivity and a high specificity compared to image grading by 3 ROP experts as the gold standard. Because missing ROP that requires treatment can have serious vision consequences, a high sensitivity is essential. The low positive predictive value is not surprising because of the low prevalence of treatment-requiring ROP.The study findings related to quality of care are particularly interesting. There were highly significant associations between the mean iROP DL score and lower birth weight and whether the unit had adequate equipment to mix and monitor oxygen. A striking finding was that only 3 of the 14 study units had enough of this equipment, which is a frequent and regrettable occurrence in a large number of units in many low- and middle-income countries.There are some important limitations of this study. iROP cannot be used to assess stage or zone, only plus disease. Whether DL and AI image analysis can achieve equally high diagnostic-test accuracy for these other prognostic signs, compared to clinical examination by indirect ophthalmoscopy or image analysis by an ROP expert, remains to be seen. In India and other low-resource settings, data on GA can be unreliable because of low attendance at antenatal care, high rates of intrauterine-growth restriction, and lack of routine prenatal ultrasound. Because GA is an important risk factor for ROP, in this study, more accurate data may have attenuated the association between iROP DL scores and quality of care. The study findings might not be generalizable to other regions. For example, the degree of retinal pigmentation, which can be greater for children born in Africa, could impact the interpretation of images.Several other groups are also working on AI for ROP screening,4–7 and once systems can provide reliable information on all of the signs needed to make a management decision at each screening episode, together with the availability of lower-cost imaging devices, the door is open for a dramatic change in how ROP screening is delivered and by whom.8In 2019, the World Health Organization and United Nations Children’s Fund recommended ROP screening and treatment as standard of care in their “Survive and Thrive” policy.9 The authors correctly indicate that integrated systems will be essential before the benefits of telemedicine and AI image analysis can be fully realized. Studies of the feasibility, acceptability, and cost-effectiveness of this approach would also be required for decision-makers and policy change within countries, with allocation of resources for implementation.

Multi-Parameter Neurological Study Based on Combined Conventional and Functional Assessments in Gaucher Disease Patients (SENOPRO_GAUCHER Study)

Gaucher Disease (GD), a metabolic inherited disorder, includes three clinical phenotypes. Type I GD (GD1), that can mimic a hematologic disease, has been classically considered as a non-neuropathic variant; type II (GD2) and type III (GD3) are classified as acute and chronic neuropathic forms, respectively. A protective role of N370S mutation against neurological impairment had been previously hypothesized in GD1, however, clinical signs of parkinsonism have recently been reported in this category of patients. The aim of our observational, monocentric, and prospective study, called SENOPRO_GAUCHER, was to evaluate in depth the neurological and neuropsychiatric aspects in GD1 patients, using clinical and instrumental investigations, in order to identify clinical and subclinical neurological manifestations, including cognitive impairment and behavioural alterations. Neurological assessments were scheduled for 22 GD patients (19 GD1 and 3 GD3) aged &amp;gt;12 years. Clinical evaluation, including Severity Scoring Tool Unified Parkinson's Disease Rating scale and Epworth Sleepiness scale; psychological tests and psychiatric evaluation, using a cognitive test battery and two psychiatric (BPRS) and psychological (CBA 2.0) questionnaires; somatosensory, motor and visual evoked potential, spectral domain optical coherence tomography (SD-OCT), standard electroretinogram; electroencephalography (EEG), and magnetic resonance (MR) 3Tesla, were planned at baseline, after 12 and 24 months. Results at the baseline evaluation of all 22 enrolled patients (9 males and 13 females; median age at the study: 44.5 years, range 17 - 68) are here reported. Regarding genotyping, all but one of the 19 GD1 pts was heterozygous for the N370S mutation. Parkinsonian motor signs were found in 10/22 patients (9 GD1): 7 pts (6 GD1) had isolated bradykinesia and 3 GD1 patients presented bradykinesia combined with rigidity. Abnormal saccadic movements were found in all GD3 pts and in one GD1. Excessive daytime sleepiness (EDS) was detected in 9/22 patients (8 GD1). EEG revealed focal, or diffuse slow waves in 6 patients (5 GD1). Significant cognitive impairments in attention, language, memory and executive functions were found in 4/19 GD1 and in 2/3 GD3 patients. Moreover, psychological and psychiatric evaluations underlined anxiety, depression and somatic concerns in 10/22 patients (9 GD1), combined with cognitive impairments in 3/12. Sixteen patients (13 GD1) showed slight or moderate sensorineural hearing loss (SNHL), and 4 of them had a cookie and reverse cookie bite morphology, typically associated with genetic SNHL. Ophthalmological evaluation revealed a degree of retinal blood vessel tortuosity in all but one patient, and superficial corneal dystrophies in 3 patients (2 GD1). An increased latency and moderate reduction in the amplitudes of optic nerve function were found in 13/22 patients (10 GD1). The SD-OCT examination showed impairment in retinal nerve fibre layers in 7 patients (6 GD1), combined with macular damage in 2 of them. Qualitative MR 3Tesla examinations revealed unspecific abnormalities in 7/19 (37%) GD1 patients; in particular, cortical and/or subcortical areas of gliosis (4 patients), vascular ectasia extended from the left frontal surface of the brain to the lateral ventricle (1 patient), dilation of perivascular spaces in the sub-cortical and nucleus-basal area (1 patient), diffuse suffering of the brain white matter due to a chronic ischemic vascular damage (1 patient). In summary, all GD3 patients showed a wide spectrum of neurological abnormalities, as expected. Surprisingly, however, all GD1 pts also presented at least two, or more, clinical and/or instrumental, neurological signs. The high prevalence of EDS in our population suggests a possible underlying sleep disorder which should be further investigated. The N370S mutation did not impact the likelihood of developing neurological abnormalities in our cohort of GD1 patients. In conclusion, our results have demonstrated that a multidisciplinary, and in-depth approach is necessary in evaluating GD1 patients. Disclosures Giona: Sanofi Genzyme: Research Funding, Speakers Bureau; Takeda: Speakers Bureau; Novartis: Research Funding.

U-COSFIRE filters for vessel tortuosity quantification with application to automated diagnosis of retinopathy of prematurity

Retinopathy of prematurity (ROP) is a sight threatening disorder that primarily affects preterm infants. It is the major reason for lifelong vision impairment and childhood blindness. Digital fundus images of preterm infants obtained from a Retcam Ophthalmic Imaging Device are typically used for ROP screening. ROP is often accompanied by Plus disease that is characterized by high levels of arteriolar tortuosity and venous dilation. The recent diagnostic procedures view the prevalence of Plus disease as a factor of prognostic significance in determining its stage, progress and severity. Our aim is to develop a diagnostic method, which can distinguish images of retinas with ROP from healthy ones and that can be interpreted by medical experts. We investigate the quantification of retinal blood vessel tortuosity via a novel U-COSFIRE (Combination Of Shifted Filter Responses) filter and propose a computer-aided diagnosis tool for automated ROP detection. The proposed methodology involves segmentation of retinal blood vessels using a set of B-COSFIRE filters with different scales followed by the detection of tortuous vessels in the obtained vessel map by means of U-COSFIRE filters. We also compare our proposed technique with an angle-based diagnostic method that utilizes the magnitude and orientation responses of the multi-scale B-COSFIRE filters. We carried out experiments on a new data set of 289 infant retinal images (89 with ROP and 200 healthy) that we collected from the programme in India called KIDROP (Karnataka Internet Assisted Diagnosis of Retinopathy of Prematurity). We used 10 images (5 with ROP and 5 healthy) for learning the parameters of our methodology and the remaining 279 images (84 with ROP and 195 healthy) for performance evaluation. We achieved sensitivity and specificity equal to 0.98 and 0.97, respectively, computed on the 279 test images. The obtained results and its explainable character demonstrate the effectiveness of the proposed approach to assist medical experts.

Recent Techniques and Trends for Retinal Blood Vessel Extraction and Tortuosity Evaluation: A Comprehensive Review

Retinal blood vessel segmentation plays an important part in the early diagnosis and treatment of eye disease. It is a tool for ophthalmologists. Many diseases can be identified by examining manifestations and images of blood vessels, including diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinopathy due to hypertension, glaucoma and others. Early detection allows physicians to provide patients with effective treatment, while in the opposite case, the late detection of retinal disease can ultimately lead to blindness. One of the indices when examining the retina is an evaluation of blood vessels based on tortuosity, i.e. the degree of curvature of blood vessels. This article presents a comprehensive overview of all segmentation techniques for retinal blood vessel extraction from images taken with a fundus camera in adults and older children or with a RetCam fundus camera in new-borns and younger children over the last 10 years. An integral part of this review is a comprehensive overview with information on all available public and private databases with retinal images. The review includes an evaluation of segmentation techniques based on objectivization parameters, including information on all objectivization parameters used in this article. As already mentioned, the degree of curvature of retinal blood vessels is used to classify severity of blood vessels tortuosity. There is no uniform metric for determining tortuosity, but this review presents a comprehensive overview of all metrics and calculations used to determine the degree of tortuosity of retinal blood vessels.

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;

Modified Curvature-based Trigonometric Identities for Retinal Blood Vessel Tortuosity Measurement in Diabetic Retinopathy Fundus Images

In current clinical practice, there is no specific standard and grading system that can be used to measure the behaviour of the retinal blood vessel curvature. The retinal blood vessel curvature is measured based on clinical experiences. It is very subjective and inconsistent to describe the presence of tortuosity in fundus images. Thus, this paper aims to measure the tortuosity of retinal blood vessel using curvature-based method and investigate its relationship with diabetic retinopathy (DR) disease. The proposed tortuosity measures have been tested on 43 fundus images belonging to patients who have been diagnosed with DR disease and validated by two clinical experts from our collaborative hospital. On average, the proposed algorithm achieved 90.7% (accuracy), 98.72% (sensitivity) and 9.3% (false negative rate), that shows significant tortuosity presence in diabetic retinopathy fundus images.

A Novel Curvature-Based Algorithm for Automatic Grading of Retinal Blood Vessel Tortuosity.

Tortuosity of retinal blood vessels is an important symptom of diabetic retinopathy or retinopathy of prematurity. In this paper, we propose an automatic image-based method for measuring single vessel and vessel network tortuosity of these vessels. Simplicity of the algorithm, low-computational burden, and an excellent matching to the clinically perceived tortuosity are the important features of the proposed algorithm. To measure tortuosity, we use curvature which is an indicator of local inflection of a curve. For curvature calculation, template disk method is a common choice and has been utilized in most of the state of the art. However, we show that this method does not possess linearity against curvature and by proposing two modifications, we improve the method. We use the basic and the modified methods to measure tortuosity on a publicly available data bank and two data banks of our own. While interpreting the results, we pursue three goals. First, to show that our algorithm is more efficient to implement than the state of the art. Second, to show that our method possesses an excellent correlation with subjective results (0.94 correlation for vessel tortuosity, 0.95 correlation for vessel network tortuosity in diabetic retinopathy, and 0.7 correlation for vessel network tortuosity in retinopathy of prematurity). Third, to show that the tortuosity perceived by an expert and curvature possess a nonlinear relation.

Automatic Evaluation of Plus Disease in Retinopathy of Prematurity

Abnormal dilation and tortuosity of retinal blood vessels are the primary signs of plus disease in retinopathy of prematurity. Timely prognosis could help reduce the delay in treatment and the risk of retinal detachment. Our objectives is to determine whether tortuosity and dilation sufficient for plus disease could be assessed most accurately by considering only arterioles, venules, or both. Tortuosity estimation and width measurement is done using previously proposed methods. Image preprocessing is applied before the two features namely, tortuosity and width of blood vessels are estimated to supply as input parameters for classification using K-means clustering technique. The results are validated by comparing with expert ophthalmologists ground truths. Performance is evaluated based on measures as sensitivity, specificity, predictive values and accuracy. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy values obtained when considering both the arteriolar tortuosity and venous dilation are 85.86%, 90.74%, 88.76%, 88.28% and 88.50% respectively.

A measure of tortuosity based on chain coding

A measure of tortuosity for 2D curves is presented. Tortuosity is a very important property of curves and has many applications, such as: how to measure the tortuosity of retinal blood vessels, intracerebral vasculature, aluminum foams, etc. The measure of tortuosity proposed here is based on a chain code called Slope Chain Code (SCC). The SCC uses some ideas which were described in [A geometric structure for 2D shapes and 3D surfaces, Pattern Recognition 25 (1992) 483-496]. The SCC of a curve is obtained by placing straight-line segments of constant length around the curve (the endpoints of the straight-line segments always touching the curve), and calculating the slope changes between contiguous straight-line segments scaled to a continuous range from −1 to 1. The SCC of a curve is independent of translation, rotation, and optionally, of scaling, which is an important advantage for computing tortuosity. Also, the minimum and maximum values of tortuosity for curves and a measure of normalized tortuosity are described. Finally, an application of the proposed measure of tortuosity is presented which corresponds to the computation of retinal blood vessel tortuosity.

An approach to localize the retinal blood vessels using bit planes and centerline detection

The change in morphology, diameter, branching pattern or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports an automated method for segmentation of blood vessels in retinal images. A unique combination of techniques for vessel centerlines detection and morphological bit plane slicing is presented to extract the blood vessel tree from the retinal images. The centerlines are extracted by using the first order derivative of a Gaussian filter in four orientations and then evaluation of derivative signs and average derivative values is performed. Mathematical morphology has emerged as a proficient technique for quantifying the blood vessels in the retina. The shape and orientation map of blood vessels is obtained by applying a multidirectional morphological top-hat operator with a linear structuring element followed by bit plane slicing of the vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The methodology is tested on three publicly available databases DRIVE, STARE and MESSIDOR. The results demonstrate that the performance of the proposed algorithm is comparable with state of the art techniques in terms of accuracy, sensitivity and specificity.

Resolution of total retinal detachment in Coats’ disease with intravitreal injection of bevacizumab

Dear Editor Coats’ disease is a nonhereditary condition of primary retinal telangiectasia that is characterized by dilation and tortuosity of retinal blood vessels, multiple aneurysms, and exudates. Coats’ disease is usually unilateral, affects young boys, and causes severe visual loss due to exudative retinal detachment. Routine photocoagulation, cryotherapy, and even surgery may have poor therapeutic effects for total retinal detachment. We report a case of Coats’ disease with total retinal detachment that demonstrated a remarkable response to intravitreal bevacizumab injection. A 3-year-old Chinese boy presented with a 2-year history of left eye squinting. The corneal light reflection test showed a 15° exotropia of the left eye, which could not be corrected. The grating visual acuity of the right eye was 0.2, and the left eye did not respond to stimuli. Examination under anesthesia showed total retinal detachment of the left eye, with extensive subretinal exudates accompanied by dilated tortuous vessels (Fig. 1a), and the intraocular pressures were 14 mmHg bilaterally. Type-B ultrasonography indicated total retinal detachment of the left eye (Fig. 2a). A diagnosis of Coats’ disease stage 3B was made via Shields’ classification [1]. After receiving consent from the boy’s parents for a trial of off-label use of bevacizumab, we performed intravitreal bevacizumab injection (1.25 mg/0.05 ml) three times at 6week intervals. Eight weeks after the last injection, the dilated vessels were alleviated, and the subretinal exudates and fluid were decreased (Fig. 1b). Type-B ultrasonography revealed a significant anatomical improvement of the retina compared with pre-injection, with only a small range of superior retinal detachment. Within 15 weeks after the last injection, the corrected visual acuity of the left eye was 20/ 125 and Type-B ultrasonography showed further resolution of the retinal detachment (Fig. 2b). Diagnosing Coats’ disease is a lengthy process, and there is still confusion about the definition. The most widely accepted diagnostic criteria of Coats’ disease include idiopathic retinal telangiectasia, intraretinal and subretinal exudation, and frequent exudative retinal detachment without vitreous or retinal traction. Coats’ disease has no clear etiopathogenisis, but it is widely accepted that it is related to a defect in the development of the retinal vessels. Vascular endothelial growth factor (VEGF) plays an important role in the development of retinal vessels. Although basic investigations of Coats’ disease are rare, it is believed that VEGF induced by hypoxia may be part of the pathogenesis, considering the evidence of elevated intravitreal VEGF levels of a patient with stage 4 Coats’ disease [2, 3]. The hypothesis suggests anti-VEGF therapies, which have been used in tumors and some neovascular diseases in ophthalmology, as possible therapies for Coats’ disease. Bevacizumab, a full-length humanized recombinant antibody against VEGF, is one of the anti-VEGF agents under consideration. There have been several encouraging reports about treating Coats’ disease with bevacizumab combined with triamcinolone injection or laser treatment [4–6]. To our knowledge, this is the first report of treating total retinal detachment caused by Coats’ disease using Financial support None.

Modeling the Tortuosity of Retinal Vessels: Does Caliber Play a Role?

The tortuosity of retinal blood vessels is a diagnostic parameter assessed by ophthalmologists on the basis of examples and experience; no quantitative model is specified in clinical practice. All quantitative measures proposed to date for automatic image analysis purposes are functions of the curvature of the vessel skeleton. We suggest in this paper that curvature may not be the only quantity involved in modeling tortuosity, and that vessel thickness, or caliber, may also play a role. To support this statement, we devise a novel measure of tortuosity, depending on both curvature and thickness, and test it with 200 vessels selected by our clinical author from the public digital retinal images for vessel extraction database. Results are in good accordance with clinical judgment. Comparative experiments show performance similar to or better than that of four measures reported in the literature. We conclude that there is reasonable evidence supporting the investigation of tortuosity models incorporating more measurements than just skeleton curvature, and specifically vessel caliber.

Measurement of retinal vascular tortuosity and its application to retinal pathologies

The tortuosity of retinal blood vessels is an important diagnostic indicator for a number of retinal pathologies. We applied robust quantitative tortuosity metrics, which are well suited to automated detection and measurement, to retinal fluorescein images of normal and diseased vessels exhibiting background diabetic retinopathy, retinitis pigmentosa and retinal vasculitis. We established the validity of the mean tortuosity (M) and the normalized root-mean-square tortuosity (K) by their strong correlation with the ranking of tortuosity by an expert panel of ophthalmologists. The low prevalences of the diseased conditions in the general population affect the classification process, and preclude the use of tortuosity for screening for all of these conditions simultaneously in the general population. Tortuosity may be useful as a screening test for retinitis alone, and may be useful for distinguishing diabetic retinopathy or vasculitis from normal in a discretionary (i.e. referred) population.

The anatomy of the rat eye with oxygen-induced retinopathy

Prior studies have documented the intertwined developmental courses of retinal blood vessel tortuosity (in fundus photographs) and retinal dysfunction (in electroretinographs) in Sprague-Dawley rat models of retinopathy of prematurity (ROP). Two such models, the "50/10 model" and the "75 model," are named after the oxygen regimens used to induce retinopathy and are characterized by distinct neurovascular courses that span a range of disease severity. In this study of 50/10 and 75 model rats, retinal flatmounts were used to study the full vasculature at postnatal day (P) 15, P19 and P30. In addition, the layers of the neural retina were measured in toluidine blue-stained cross sections. Finally, gross anatomic features of the eye, including axial length, retinal surface area, and the ratio of anterior to posterior axial-lengths were evaluated. Both clock hours of neovascularization (NV) and percent avascular retina (AR) peaked at P19 and resolved by P30. Through P19, NV was found in every 50/10 model rat, but in only 60% of 75 model rats. AR was positively related to NV. All inner layers of the retina (outer plexiform layer through ganglion cell layer) were attenuated in 50/10 model rats but, in the 75 model, no layer differed significantly from that in controls. The eyes in both ROP models were smaller than those of age-matched controls. The ratio of anterior to posterior axial-lengths ranged from 0.45 in controls through 0.37 in the 75 model to 0.32 in the 50/10 model. Thus, eye growth is altered in these rat models of ROP.

COMPUTER-ASSISTED ASSESSMENT OF PLUS DISEASE IN RETINOPATHY OF PREMATURITY USING VIDEO INDIRECT OPHTHALMOSCOPY IMAGES

"ROPtool" is a computer program that measures retinal blood vessel tortuosity. Our aim was to determine the feasibility and accuracy of analyzing images with ROPtool, which were obtained using video indirect ophthalmoscopy. Forty-five posterior pole still images captured from indirect ophthalmoscopy video clips were selected; 20 were selected for high quality and 25 were randomly selected. One of the authors (S.A.) used ROPtool to measure tortuosity for each quadrant of each image. Two of the authors (D.K.W. and S.F.F.) independently judged tortuosity on a 10-point scale, and their averaged grades were used as the reference standard. Among randomly selected images, ROPtool was able to trace at least two major vessels in 43 of 100 quadrants (43%). Lighter fundus pigment color was associated with ROPtool's ability to analyze images (P = 0.004). When considering analyzable images only, ROPtool's sensitivity in detecting tortuosity sufficient for plus disease was 83% (5/6) and specificity was 90% (18/20). ROPtool's sensitivity for pre-plus tortuosity was 100% (9/9) and specificity was 71% (12/17). ROPtool is useful for analyzing video indirect ophthalmoscopy images only when applied to those with high quality. When analyzing these images, ROPtool has very good accuracy compared to consensus of experienced examiners.