A Review on the Extraction of Quantitative Retinal Microvascular Image Feature

Autosomal recessive cerebellar ataxias: a diagnostic classification approach according to ocular features

Autosomal recessive cerebellar ataxias (ARCAs) are a heterogeneous group of neurodegenerative disorders affecting primarily the cerebellum and/or its afferent tracts, often accompanied by damage of other neurological or extra-neurological systems. Due to the overlap of clinical presentation among ARCAs and the variety of hereditary, acquired, and reversible etiologies that can determine cerebellar dysfunction, the differential diagnosis is challenging, but also urgent considering the ongoing development of promising target therapies. The examination of afferent and efferent visual system may provide neurophysiological and structural information related to cerebellar dysfunction and neurodegeneration thus allowing a possible diagnostic classification approach according to ocular features. While optic coherence tomography (OCT) is applied for the parametrization of the optic nerve and macular area, the eye movements analysis relies on a wide range of eye-tracker devices and the application of machine-learning techniques. We discuss the results of clinical and eye-tracking oculomotor examination, the OCT findings and some advancing of computer science in ARCAs thus providing evidence sustaining the identification of robust eye parameters as possible markers of ARCAs.

CPMF-Net: Multi-Feature Network Based on Collaborative Patches for Retinal Vessel Segmentation

As an important basis of clinical diagnosis, the morphology of retinal vessels is very useful for the early diagnosis of some eye diseases. In recent years, with the rapid development of deep learning technology, automatic segmentation methods based on it have made considerable progresses in the field of retinal blood vessel segmentation. However, due to the complexity of vessel structure and the poor quality of some images, retinal vessel segmentation, especially the segmentation of Capillaries, is still a challenging task. In this work, we propose a new retinal blood vessel segmentation method, called multi-feature segmentation, based on collaborative patches. First, we design a new collaborative patch training method which effectively compensates for the pixel information loss in the patch extraction through information transmission between collaborative patches. Additionally, the collaborative patch training strategy can simultaneously have the characteristics of low occupancy, easy structure and high accuracy. Then, we design a multi-feature network to gather a variety of information features. The hierarchical network structure, together with the integration of the adaptive coordinate attention module and the gated self-attention module, enables these rich information features to be used for segmentation. Finally, we evaluate the proposed method on two public datasets, namely DRIVE and STARE, and compare the results of our method with those of other nine advanced methods. The results show that our method outperforms other existing methods.

BCR-UNet: Bi-directional ConvLSTM residual U-Net for retinal blood vessel segmentation

Background

High precision segmentation of retinal blood vessels from retinal images is a significant step for doctors to diagnose many diseases such as glaucoma and cardiovascular diseases. However, at the peripheral region of vessels, previous U-Net-based segmentation methods failed to significantly preserve the low-contrast tiny vessels.

Methods

For solving this challenge, we propose a novel network model called Bi-directional ConvLSTM Residual U-Net (BCR-UNet), which takes full advantage of U-Net, Dropblock, Residual convolution and Bi-directional ConvLSTM (BConvLSTM). In this proposed BCR-UNet model, we propose a novel Structured Dropout Residual Block (SDRB) instead of using the original U-Net convolutional block, to construct our network skeleton for improving the robustness of the network. Furthermore, to improve the discriminative ability of the network and preserve more original semantic information of tiny vessels, we adopt BConvLSTM to integrate the feature maps captured from the first residual block and the last up-convolutional layer in a nonlinear manner.

Results and discussion

We conduct experiments on four public retinal blood vessel datasets, and the results show that the proposed BCR-UNet can preserve more tiny blood vessels at the low-contrast peripheral regions, even outperforming previous state-of-the-art methods.

An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.

Hybrid high-definition microvessel imaging/shear wave elastography improves breast lesion characterization

BACKGROUND

Low specificity in current breast imaging modalities leads to increased unnecessary follow-ups and biopsies. The purpose of this study is to evaluate the efficacy of combining the quantitative parameters of high-definition microvasculature imaging (HDMI) and 2D shear wave elastography (SWE) with clinical factors (lesion depth and age) for improving breast lesion differentiation.

METHODS

In this prospective study, from June 2016 through April 2021, patients with breast lesions identified on diagnostic ultrasound and recommended for core needle biopsy were recruited. HDMI and SWE were conducted prior to biopsies. Two new HDMI parameters, Murray's deviation and bifurcation angle, and a new SWE parameter, mass characteristic frequency, were included for quantitative analysis. Lesion malignancy prediction models based on HDMI only, SWE only, the combination of HDMI and SWE, and the combination of HDMI, SWE and clinical factors were trained via elastic net logistic regression with 70% (360/514) randomly selected data and validated with the remaining 30% (154/514) data. Prediction performances in the validation test set were compared across models with respect to area under the ROC curve as well as sensitivity and specificity based on optimized threshold selection.

RESULTS

A total of 508 participants (mean age, 54 years ± 15), including 507 female participants and 1 male participant, with 514 suspicious breast lesions (range, 4-72 mm, median size, 13 mm) were included. Of the lesions, 204 were malignant. The SWE-HDMI prediction model, combining quantitative parameters from SWE and HDMI, with AUC of 0.973 (95% CI 0.95-0.99), was significantly higher than the result predicted with the SWE model or HDMI model alone. With an optimal cutoff of 0.25 for the malignancy probability, the sensitivity and specificity were 95.5% and 89.7%, respectively. The specificity was further improved with the addition of clinical factors. The corresponding model defined as the SWE-HDMI-C prediction model had an AUC of 0.981 (95% CI 0.96-1.00).

CONCLUSIONS

The SWE-HDMI-C detection model, a combination of SWE estimates, HDMI quantitative biomarkers and clinical factors, greatly improved the accuracy in breast lesion characterization.

Quantitative Biomarkers for Cancer Detection Using Contrast-Free Ultrasound High-Definition Microvessel Imaging: Fractal Dimension, Murray's Deviation, Bifurcation Angle & Spatial Vascularity Pattern

A growing body of evidence indicates that there is a strong correlation between microvascular morphological features and malignant tumors. Therefore, quantification of these features might allow more accurate differentiation of benign and malignant tumors. The main objective of this research project is to improve the quantification of microvascular networks depicted in contrast-free ultrasound microvessel images. To achieve this goal, a new series of quantitative microvessel morphological parameters are introduced for differentiation of breast masses using contrast-free ultrasound-based high-definition microvessel imaging (HDMI). Using HDMI, we quantified and analyzed four new parameters: 1) microvessel fractal dimension (mvFD), a marker of tumor microvascular complexity; 2) Murray's deviation (MD), the diameter mismatch, defined as the deviation from Murray's law; 3) bifurcation angle (BA), abnormally decreased angle; and 4) spatial vascular pattern (SVP), indicating tumor vascular distribution pattern, either intratumoral or peritumoral. The new biomarkers have been tested on 60 patients with breast masses. Validation of the feature's extraction algorithm was performed using a synthetic data set. All the proposed parameters had the power to discriminate the breast lesion malignancy (p < 0.05), displaying BA as the most sensitive test, with a sensitivity of 90.6%, and mvFD as the most specific test, with a specificity of 92%. The results of all four new biomarkers showed an AUC = 0.889, sensitivity of 80% and specificity of 91.4% In conclusion, the added value of the proposed quantitative morphological parameters, as new biomarkers of angiogenesis within breast masses, paves the way for more accurate breast cancer detection with higher specificity.

2D alpha-shapes to quantify retinal microvasculature morphology and their application to proliferative diabetic retinopathy characterisation in fundus photographs

The use of 2D alpha-shapes (α-shapes) to quantify morphological features of the retinal microvasculature could lead to imaging biomarkers for proliferative diabetic retinopathy (PDR). We tested our approach using the MESSIDOR dataset that consists of colour fundus photographs from 547 healthy individuals, 149 with mild diabetic retinopathy (DR), 239 with moderate DR, 199 pre-PDR and 53 PDR. The skeleton (centrelines) of the automatically segmented retinal vasculature was represented as an α-shape and the proposed parameters, complexity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Op\alpha }_{min}$$\end{document}Opαmin), spread (OpA), global shape (VS) and presence of abnormal angiogenesis (Grad_α) were computed. In cross-sectional analysis, individuals with PDR had a lower \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Op\alpha }_{min}$$\end{document}Opαmin, OpA and Grad_α indicating a vasculature that is more complex, less spread (i.e. dense) and the presence of numerous small vessels. The results show that α-shape parameters characterise vascular abnormalities predictive of PDR (AUC 0.73; 95% CI [0.73 0.74]) and have therefore potential to reveal changes in retinal microvascular morphology.

Tortuosity Index Calculations in Retinal Images: Some Criticalities Arising from Commonly Used Approaches

A growing body of research in retinal imaging is recently considering vascular tortuosity measures or indexes, with definitions and methods mostly derived from cardiovascular research. However, retinal microvasculature has its own peculiarities that must be considered in order to produce reliable measurements. This study analyzed and compared various derived metrics (e.g., TI, TI_avg, TI*CV) across four existing computational workflows. Specifically, the implementation of the models on two critical OCT images highlighted main pitfalls of the methods, which may fail in reliably differentiating a highly tortuous image from a normal one. A tentative, encouraging approach to mitigate the issue on the same OCT exemplificative images is described in the paper, based on the suggested index TI*CV.

Multimodal Retinal Imaging for Detection of Ischemic Stroke

Background: This study aims to evaluate ocular changes in patients with ischemic stroke using multimodal imaging and explore the predictive value of ocular abnormalities for ischemic stroke.

Methods: A total of 203 patients (ischemic stroke group, 62; control group, 141) were enrolled in this study. Basic data from patients, including age; gender; height; weight; history of hypertension, hyperlipidemia, diabetes, alcohol use, and coronary heart disease; and smoking status, were collected. Consequently, Doppler color ultrasound, color fundus photography, and optical coherence tomography (OCT) examinations were conducted. Differences in traditional risk factors and ocular parameters between the two groups were compared, and binary logistic regression was used for multivariate analysis.

Results: The central retinal artery equivalent (CRAE) in the ischemic stroke group was 150.72 ± 20.15 μm and that in the control group was 159.68 ± 20.05 μm. The difference was statistically significant (P = 0.004). Moreover, the subfoveal choroidal thickness (SFChT) in the ischemic stroke group was 199.90 ± 69.27 μm and that in the control group was 227.40 ± 62.20 μm. The difference was statistically significant (P = 0.006). Logistic regression results showed that smoking [odds ratio (OR) = 2.823; 95% confidence interval (95% CI) = 1.477–5.395], CRAE (OR = 0.980; 95% CI = 0.965–0.996), and SFChT (OR = 0.994; 95% CI = 0.989–0.999) are associated with increased risk of ischemic stroke when ocular parameters were combined with traditional risk factors. The area under the receiver operating characteristic (ROC) curve was 0.726, which shows good diagnostic accuracy.

Conclusion: SFChT may be a diagnostic marker for early detection and monitoring of ischemic stroke. Combined with traditional risks, retinal artery diameter, and choroidal thickness, the prediction model can improve ischemic stroke prediction.

Retinal vessel changes in cerebrovascular disease

PURPOSE OF REVIEW

The retina is growingly recognized as a window into cerebrovascular and systemic vascular conditions. The utility of noninvasive retinal vessel biomarkers in cerebrovascular risk assessment has expanded due to advances in retinal imaging techniques and machine learning-based digital analysis. The purpose of this review is to underscore the latest evidence linking retinal vascular abnormalities with stroke and vascular-related cognitive disorders; to highlight modern developments in retinal vascular imaging modalities and software-based vasculopathy quantification.

RECENT FINDINGS

Longitudinal studies undertaken for extended periods indicate that retinal vascular changes can predict cerebrovascular disorders (CVD). Cerebrovascular ties to dementia provoked recent explorations of retinal vessel imaging tools for conceivable early cognitive decline detection. Innovative biomedical engineering technologies and advanced dynamic and functional retinal vascular imaging methods have recently been added to the armamentarium, allowing an unbiased and comprehensive analysis of the retinal vasculature. Improved artificial intelligence-based deep learning algorithms have boosted the application of retinal imaging as a clinical and research tool to screen, risk stratify, and monitor with precision CVD and vascular cognitive impairment.

SUMMARY

Mounting evidence supports the use of quantitative retinal vessel analysis in predicting CVD, from clinical stroke to neuroimaging markers of stroke and neurodegeneration.

Associations of retinal microvascular caliber with large arterial function and structure: A population-based study of 11 to 12 year-olds and midlife adults

OBJECTIVE

We examined associations between retinal microvascular and large arterial phenotypes to explore relationships between the micro- and macro-vasculature in childhood and midlife.

METHODS

Participants were 1288 children (11-12 years, 50.9% female) and 1264 adults (mean age 44 years, 87.6% female) in a cross-sectional population-based study. Exposures were retinal arteriolar and venular caliber quantified from retinal images. Outcomes included arterial function (pulse wave velocity; carotid arterial elasticity) and structure (carotid intima-media thickness). Multivariable regression models were performed adjusting for age, sex, and family socioeconomic position.

RESULTS

In children, one standard deviation wider arteriolar caliber was associated with slower pulse wave velocity (-0.15 SD, 95% CI -0.21, -0.09) and higher elasticity (0.13 SD, 95% CI 0.06, 0.20); per SD wider venular caliber was associated with faster pulse wave velocity (0.09 SD, 95% CI 0.03, 0.15) and lower elasticity (-0.07 SD, 95% CI -0.13, -0.01). The size of adult associations was approximately double. Wider arteriolar caliber was associated with smaller carotid intima-media thickness (-0.09 SD, 95% CI -0.16, -0.03) in adults but not children. Venular caliber and carotid intima-media thickness showed little evidence of association.

CONCLUSIONS

Narrower retinal arterioles and wider venules are associated with large arterial function as early as mid-childhood. Associations strengthen by midlife and also extend to arterial structure, although effect sizes remain small.

Chinese ASCVD risk equations rather than pooled cohort equations are better to identify macro- and microcirculation abnormalities

BACKGROUND

We hypothesized that discriminating the early subclinical organ damage would serve as a great opportunity for prevention against atherosclerotic cardiovascular disease (ASCVD). Brachial-ankle pulse wave velocity (baPWV), low retinal vascular fractal dimension, and albuminuria are surrogates of subclinical vascular changes.

METHODS

The aim of this study was to use Pooled Cohort Equations (PCE) and ASCVD risk equations derived from "Prediction for ASCVD Risk in China project (CHINA-PAR)" to observe the prevalence of macro- and microcirculation abnormalities. A total of 2166 subjects were involved. Characteristics were investigated using questionnaire and physical examinations. We calculated the urine albumin to creatinine ratio (UACR). The baPWV was measured using a fully automatic arteriosclerosis detector. The retinal vascular fractal dimension was measured by a semiautomated computer-based program. The 10-year ASCVD risk was estimated using the PCE and CHINA-PAR model.

RESULTS

The cut-off values for the elevated baPWV were 2.82 and 2.92% in the PCE model and CHINA-PAR model, respectively, with nearly 85% sensitivity and an average specificity of 74%. For low retinal fractal dimension, at the cut-off point of 3.8%, we acquired an acceptable sensitivity of 66.27-68.24% and specificity of 62.57-67.45%. All the C-statistics presented a significant improvement from the PCE model to the CHINA-PAR model (P < 0.05). For all categories-net reclassification improvement (NRI) values were significant and clearly varied (0.329, 0.183, and 0.104, respectively) depending on the cut-off set at 3%.

CONCLUSION

Our study demonstrated that the CHINA-PAR equations rather than PCE could provide better identification of macro- and microcirculation abnormalities. A lower cut-off point for the subclinical vascular changes may be selected in a population from southeast China.

A Review of Hypertensive Retinopathy and Chorioretinopathy

Hypertensive retinopathy and choroidopathy have important short- and long-term implications on patients' overall health and mortality. Eye care professionals should be familiar with the severity staging of these entities and be able to readily recognize and refer patients who are in need of systemic blood pressure control. This paper will review the diagnosis, staging, treatment, and long-term implications for vision and mortality of patients with hypertensive retinopathy and choroidopathy.

Microvascular imaging of the skin

Despite our understanding that the microvasculature plays a multifaceted role in the development and progression of various conditions, we know little about the extent of this involvement. A need exists for non-invasive, clinically meaningful imaging modalities capable of elucidating microvascular information to aid in our understanding of disease, and to aid in the diagnosis/monitoring of disease for more patient-specific care. In this review article, a number of imaging techniques are summarized that have been utilized to investigate the microvasculature of skin, along with their advantages, disadvantages and future perspectives in preclinical and clinical settings. These techniques include dermoscopy, capillaroscopy, Doppler sonography, laser Doppler flowmetry (LDF) and perfusion imaging, laser speckle contrast imaging (LSCI), optical coherence tomography (OCT), including its Doppler and dynamic variant and the more recently developed OCT angiography (OCTA), photoacoustic imaging, and spatial frequency domain imaging (SFDI). Attention is largely, but not exclusively, placed on optical imaging modalities that use intrinsic optical signals to contrast the microvasculature. We conclude that whilst each imaging modality has been successful in filling a particular niche, there is no one, all-encompassing modality without inherent flaws. Therefore, the future of cutaneous microvascular imaging may lie in utilizing a multi-modal approach that will counter the disadvantages of individual systems to synergistically augment our imaging capabilities.