Regional Patterns in Retinal Microvascular Network Geometry in Health and Disease

Angio-Net: deep learning-based label-free detection and morphometric analysis of in vitro angiogenesis

Despite significant advancements in three-dimensional (3D) cell culture technology and the acquisition of extensive data, there is an ongoing need for more effective and dependable data analysis methods. These concerns arise from the continued reliance on manual quantification techniques. In this study, we introduce a microphysiological system (MPS) that seamlessly integrates 3D cell culture to acquire large-scale imaging data and employs deep learning-based virtual staining for quantitative angiogenesis analysis. We utilize a standardized microfluidic device to obtain comprehensive angiogenesis data. Introducing Angio-Net, a novel solution that replaces conventional immunocytochemistry, we convert brightfield images into label-free virtual fluorescence images through the fusion of SegNet and cGAN. Moreover, we develop a tool capable of extracting morphological blood vessel features and automating their measurement, facilitating precise quantitative analysis. This integrated system proves to be invaluable for evaluating drug efficacy, including the assessment of anticancer drugs on targets such as the tumor microenvironment. Additionally, its unique ability to enable live cell imaging without the need for cell fixation promises to broaden the horizons of pharmaceutical and biological research. Our study pioneers a powerful approach to high-throughput angiogenesis analysis, marking a significant advancement in MPS.

Application of ImageJ in Optical Coherence Tomography Angiography (OCT-A): A Literature Review

Background

This study aimed to review the literature on the application of ImageJ in optical coherence tomography angiography (OCT-A) images.

Methods

A general search was performed in PubMed, Google Scholar, and Scopus databases. The authors evaluated each of the selected articles in order to assess the implementation of ImageJ in OCT-A images.

Results

ImageJ can aid in reducing artifacts, enhancing image quality to increase the accuracy of the process and analysis, processing and analyzing images, generating comparable parameters such as the parameters that assess perfusion of the layers (vessel density (VD), skeletonized density (SD), and vessel length density (VLD)) and the parameters that evaluate the structure of the layers (fractal dimension (FD), vessel density index (VDI), and lacunarity (LAC)), and the foveal avascular zone (FAZ) that are used widely in the retinal and choroidal studies), and establishing diagnostic criteria. It can help to save time when the dataset is huge with numerous plugins and options for image processing and analysis with reliable results. Diverse studies implemented distinct binarization and thresholding techniques, resulting in disparate outcomes and incomparable parameters. Uniformity in methodology is required to acquire comparable data from studies employing diverse processing and analysis techniques that yield varied outcomes.

Conclusion

Researchers and professionals might benefit from using ImageJ because of how quickly and correctly it processes and analyzes images. It is highly adaptable and potent software, allowing users to evaluate images in a variety of ways. There exists a diverse range of methodologies for analyzing OCTA images through the utilization of ImageJ. However, it is imperative to establish a standardized strategy to ensure the reliability and consistency of the method for research purposes.

Metabolic blood flow regulation in a hybrid model of the human retinal microcirculation

The retinal vascular network supplies perfusion to vital visual structures, including retinal ganglion cells responsible for vision. Impairments in retinal blood flow and oxygenation are involved in the progression of many ocular diseases, including glaucoma. In this study, an established theoretical hybrid model of a retinal microvascular network is extended to include the effects of local blood flow regulation on oxygenation. A heterogeneous representation of the arterioles based on confocal microscopy images is combined with a compartmental description of the downstream capillaries and venules. A Green's function method is used to simulate oxygen transport in the arterioles, and a Krogh cylinder model is applied to the capillary and venular compartments. Acute blood flow regulation is simulated in response to changes in pressure, shear stress, and metabolism. Model results predict that both increased intraocular pressure and impairment of blood flow regulation can cause decreased tissue oxygenation, indicating that both mechanisms represent factors that could lead to impaired oxygenation characteristic of ocular disease. Results also indicate that the metabolic response mechanism reduces the fraction of poorly oxygenated tissue but that the pressure- and shear stress-dependent response mechanisms may hinder the vascular response to changes in oxygenation. Importantly, the heterogeneity of the vascular network demonstrates that traditionally reported average values of tissue oxygen levels hide significant localized defects in tissue oxygenation that may be involved in disease processes, including glaucoma. Ultimately, the model framework presented in this study will facilitate future comparisons to sectorial-specific clinical data to better assess the role of impaired blood flow regulation in ocular disease.

RV-ESA: A novel computer-aided elastic shape analysis system for retinal vessels in diabetic retinopathy

Diabetic retinopathy (DR), one of the most common and serious complications of diabetes, has become one of the main blindness diseases. The retinal vasculature is the only part of the human circulatory system that allows direct noninvasive visualization of the body's microvasculature, which provides the opportunity to detect the structural and functional changes before DR becomes unable to intervene. For decades, as the fundamental step in computer-assisted analysis of retinopathy, retinal vascular extraction methods have been largely developed. However, further research focusing on retinal vascular analysis is still in its infancy. Meanwhile, due to the complexity of retinal vascular structure, the relationship between vascular geometry and DR has never been concluded. This paper aims to provide a novel computer-aided shape analysis system for retinal vessels. To perform retinal vascular shape analysis, a mathematical geometric representation is firstly generated by utilizing the proposed shape modeling method. Then, several useful statistical tools (e.g. Graph Mean, Graph PCA) are adopted to quantitatively analyze the vascular shape. Besides, in order to visualize the changes in vascular shape in the progression of DR, a geodesic tool is used to display the deformation process for ophthalmologists to observe. The efficacy of this analysis system is demonstrated in the EyePACS dataset and the subsequent visit records of 98 patients from the proprietary dataset. The experimental results show that there is a certain correlation between the variation of retinal vascular shape and DR progression, and the Graph PCA scores of retinal vessels are negatively correlated with DR grades. The code of our RV-ESA system can be publicly available at github.com/XiaolingLuo/RV-ESA.

Alterations in Coronary Resistance Artery Network Geometry in Diabetes and the Role of Tenascin C

Background

Geometrical alterations in the coronary resistance artery network and the potential involvement of Tenascin C (TNC) extracellular matrix protein were investigated in diabetic and control mice.

Methods

Diabetes was induced by streptozotocin (STZ) injections (n = 7-11 animals in each group) in Tenascin C KO (TNC KO) mice and their Wild type (A/J) littermates. After 16-18 weeks the heart was removed and the whole subsurface network of the left coronary artery was prepared (down to branches of 40 μ m outer diameter), in situ pressure-perfused and studied using video-microscopy. Outer and inner diameters, wall thicknesses and bifurcation angles were measured on whole network pictures reconstructed into collages at 1.7 μ m pixel resolutions.

Results

Diabetes induced abnormal morphological alterations including trifurcations, sharp bends of larger branches, and branches directed retrogradely (p < 0.001 by the χ 2 test). Networks of TNC KO mice tended to form early divisions producing parallelly running larger branches (p < 0.001 by the χ 2 probe). Networks of coronary resistance arteries were substantially more abundant in 100-180 μ m components, appearing in 2-5 mm flow distance from orifice in diabetes. This was accompanied by thickening of the wall of larger arterioles ( > 220 μ m) and thinning of the wall of smaller (100-140 μ m) arterioles (p < 0.001). Blood flow should cover larger distances in diabetic networks, but interestingly STZ-induced diabetes did not generate further geometrical changes in TNC KO mice.

Conclusions

Diabetes promotes hypertrophic and hypotrophic vascular remodeling and induces vasculogenesis at well defined, specific positions of the coronary vasculature. TNC plays a pivotal role in the formation of coronary network geometry, and TNC deletion causes parallel fragmentation preventing diabetes-induced abnormal vascular morphologies.

Design and Implementation of Anatomically Inspired Mesenteric and Intestinal Vascular Patterns for Personalized 3D Bioprinting

Recent progress in bioprinting has made possible the creation of complex 3D intestinal constructs, including vascularized villi. However, for their integration into functional units useful for experimentation or implantation, the next challenge is to endow them with a larger-scale, anatomically realistic vasculature. In general, the perfusion of bioprinted constructs has remained difficult, and the current solution is to provide them with mostly linear and simply branched channels. To address this limitation, here we demonstrated an image analysis-based workflow leading through computer-assisted design from anatomic images of rodent mesentery and colon to the actual printing of such patterns with paste and hydrogel bioinks. Moreover, we reverse-engineered the 2D intestinal image-derived designs into cylindrical objects, and 3D-printed them in a support hydrogel. These results open the path towards generation of more realistically vascularized tissue constructs for a variety of personalized medicine applications.

Metabolic Signaling in a Theoretical Model of the Human Retinal Microcirculation

Impaired blood flow and oxygenation contribute to many ocular pathologies, including glaucoma. Here, a mathematical model is presented that combines an image-based heterogeneous representation of retinal arterioles with a compartmental description of capillaries and venules. The arteriolar model of the human retina is extrapolated from a previous mouse model based on confocal microscopy images. Every terminal arteriole is connected in series to compartments for capillaries and venules, yielding a hybrid model for predicting blood flow and oxygenation throughout the retinal microcirculation. A metabolic wall signal is calculated in each vessel according to blood and tissue oxygen levels. As expected, a higher average metabolic signal is generated in pathways with a lower average oxygen level. The model also predicts a wide range of metabolic signals dependent on oxygen levels and specific network location. For example, for high oxygen demand, a threefold range in metabolic signal is predicted despite nearly identical PO2 levels. This whole-network approach, including a spatially nonuniform structure, is needed to describe the metabolic status of the retina. This model provides the geometric and hemodynamic framework necessary to predict ocular blood flow regulation and will ultimately facilitate early detection and treatment of ischemic and metabolic disorders of the eye.

TREND database: Retinal images of healthy young subjects visualized by a portable digital non-mydriatic fundus camera

Topological characterization of the Retinal microvascular nEtwork visualized by portable fuNDus camera (TREND) is a database comprising of 72 color digital retinal images collected from the students of the Faculty of Medicine at the University of Montenegro, in the period from February 18th to March 11th 2020. The database also includes binarized images of manually segmented microvascular networks associated with each raw image. The participant demographic characteristics, health status, and social habits information such as age, sex, body mass index, smoking history, alcohol use, as well as previous medical history was collected. As proof of the concept, a smaller set of 10 color digital fundus images from healthy older participants is also included. Comparison of the microvascular parameters of these two sets of images demonstrate that digital fundus images recorded with a hand-held portable camera are able to capture the changes in patterns of microvascular network associated with aging. The raw images from the TREND database provide a standard that defines normal retinal anatomy and microvascular network geometry in young healthy people in Montenegro as it is seen with the digital hand-held portable non-mydriatic MiiS HORUS Scope DEC 200.This knowledge could facilitate the application of this technology at the primary level of health care for large scale telematic screening for complications of chronic diseases, such as hypertensive and diabetic retinopathy. In addition, it could aid in the development of new methods for early detection of age-related changes in the retina, systemic chronic diseases, as well as eye-specific diseases. The associated manually segmented images of the microvascular networks provide the standard that can be used for development of automatic software for image quality assessment, segmentation of microvascular network, and for computer-aided detection of pathological changes in retina. The TREND database is freely available at https://doi.org/10.5281/zenodo.4521043.

Retinal vascular geometry detection as a biomarker in diabetes mellitus

OBJECTIVE

To compare the vessel geometry characteristics of color fundus photographs in normal control and diabetes mellitus (DM) patients and to find potential biomarkers for early diabetic retinopathy (DR) based on a neural network vessel segmentation system and automated vascular geometry parameter analysis software.

METHODS

A total of 102 consecutive patients with type 2 DM (T2DM) and 132 healthy controls were recruited. All participants underwent general ophthalmic examinations, and retinal fundus photographs were taken with a digital fundus camera without mydriasis. Color fundus photographs were input into a dense-block generative adversarial network (D-GAN)-assisted retinal vascular segmentation system (http://www.gdcerc.cn:8081/#/login) to obtain binary images. These images were then analyzed by customized software (ocular microvascular analysis system V2.9.1) for automatic processing of vessel geometry parameters, including the monofractal dimension (D_box), multifractal dimension (D₀), vessel area ratio (R), max vessel diameter (d_max), average vessel diameter (d_ave), arc-chord ratio (A/C), and tortuosity (τ_n). Geometric differences between the healthy subjects and DM patients were analyzed. Then, regression analysis and receiver operating characteristic (ROC) curve analysis were performed to evaluate the diagnostic efficiency of the vascular geometry parameters.

RESULTS

No significant differences were observed between the baseline characteristics of each group. DM patients had lower D_box and D₀ values (1.330 ± 0.041; 1.347 ± 0.038) than healthy subjects (1.343 ± 0.048, p < 0.05; 1.362 ± 0.042, p < 0.05) and showed increasing values of d_max, d_ave, A/C, and τ_n compared with normal controls, although only the differences in d_ave and τ_n between the groups were statistically significant. In the regression analysis, d_ave and τ_n showed a good correlation with diabetes (d_ave, OR 1.765, 95% CI 1.319-2.362, p < 0.001; τ_n, OR 9.323, 95% CI 1.492-58.262, p < 0.05).

CONCLUSIONS

We demonstrated the relationship between retinal vascular geometry and the process in DM patients, showing that D_box, D₀, d_ave, and τ_n may be indicators of morphological changes in retinal vessels in DM patients and can be early biomarkers of DR.

Numerical Computational Study of Photoacoustic Signals from Eye Models to Detect Diabetic Retinopathy

Introduction:

Detection of Diabetic Retinopathy (DR) is essential in clinical ophthalmology as it may prevent sight degradation. In this paper, a complete Photoacoustic (PA) analysis is implemented to detect DR in three different eye models representing a healthy eye as well as two abnormal eyes exhibiting Non-Proliferative Retinopathy (NPDR), and Proliferative Retinopathy (PDR)

Methods & Materials:

Monte Carlo method was used to simulate the interaction of a 0.8 ns duration laser pulse with eye tissues at 750 nm wavelength. Thermal, structural and acoustical analyses were performed using the Finite Element Method (FEM).

Results:

The results showed that there is a significant change in the amplitude of the detected PA signal for abnormal eye tissues in the retina (P < 0.05) as compared to healthy eye tissues. The maximum amplitude of the received PA signal in the NPDR and the PDR eye models is 5% and 33%, respectively, which are greater than those observed in the healthy eye.

Conclusion:

These results may provide insights into using PA imaging to detect DR.