3D Vascular Network Research Articles

Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning.

Mesoscopic photoacoustic imaging (PAI) enables label-free visualization of vascular networks in tissues with high contrast and resolution. Segmenting these networks from 3D PAI data and interpreting their physiological and pathological significance is crucial yet challenging due to the time-consuming and error-prone nature of current methods. Deep learning offers a potential solution; however, supervised analysis frameworks typically require human-annotated ground-truth labels. To address this, an unsupervised image-to-image translation deep learning model is introduced, the Vessel Segmentation Generative Adversarial Network (VAN-GAN). VAN-GAN integrates synthetic blood vessel networks that closely resemble real-life anatomy into its training process and learns to replicate the underlying physics of the PAI system in order to learn how to segment vasculature from 3D photoacoustic images. Applied to a diverse range of in silico, in vitro, and in vivo data, including patient-derived breast cancer xenograft models and 3D clinical angiograms, VAN-GAN demonstrates its capability to facilitate accurate and unbiased segmentation of 3D vascular networks. By leveraging synthetic data, VAN-GAN reduces the reliance on manual labeling, thus lowering the barrier to entry for high-quality blood vessel segmentation (F1 score: VAN-GAN vs. U-Net = 0.84 vs. 0.87) and enhancing preclinical and clinical research into vascular structure andfunction.

On the influence of the vascular architecture on Gradient Echo and Spin Echo BOLD fMRI signals across cortical depth: a simulation approach based on realistic 3D vascular networks.

GE-BOLD contrast stands out as the predominant technique in functional MRI experiments for its high sensitivity and straightforward implementation. GE-BOLD exhibits rather similar sensitivity to vessels independent of their size at submillimeter resolution studies like those examining cortical columns and laminae. However, the presence of nonspecific macrovascular contributions poses a challenge to accurately isolate neuronal activity. SE-BOLD increases specificity towards small vessels, thereby enhancing its specificity to neuronal activity, due to the effective suppression of extravascular contributions caused by macrovessels with its refocusing pulse. However, even SE-BOLD measurements may not completely remove these macrovascular contributions. By simulating hemodynamic signals across cortical depth, we gain insights into vascular contributions to the laminar BOLD signal. In this study, we employed four realistic 3D vascular models to simulate oxygen saturation states in various vascular compartments, aiming to characterize both intravascular and extravascular contributions to GE and SE signals, and corresponding BOLD signal changes, across cortical depth at 7T. Simulations suggest that SE-BOLD cannot completely reduce the macrovascular contribution near the pial surface. Simulations also show that both the specificity and signal amplitude of BOLD signals at 7T depend on the spatial arrangement of large vessels throughout cortical depth and on the pial surface.

3DVascNet: An Automated Software for Segmentation and Quantification of Mouse Vascular Networks in 3D.

Analysis of vascular networks is an essential step to unravel the mechanisms regulating the physiological and pathological organization of blood vessels. So far, most of the analyses are performed using 2-dimensional projections of 3-dimensional (3D) networks, a strategy that has several obvious shortcomings. For instance, it does not capture the true geometry of the vasculature and generates artifacts on vessel connectivity. These limitations are accepted in the field because manual analysis of 3D vascular networks is a laborious and complex process that is often prohibitive for large volumes. To overcome these issues, we developed 3DVascNet, a deep learning-based software for automated segmentation and quantification of 3D retinal vascular networks. 3DVascNet performs segmentation based on a deep learning model, and it quantifies vascular morphometric parameters such as vessel density, branch length, vessel radius, and branching point density. We tested the performance of 3DVascNet using a large data set of 3D microscopy images of mouse retinal blood vessels. We demonstrated that 3DVascNet efficiently segments vascular networks in 3D and that vascular morphometric parameters capture phenotypes detected by using manual segmentation and quantification in 2 dimension. In addition, we showed that, despite being trained on retinal images, 3DVascNet has high generalization capability and successfully segments images originating from other data sets and organs. Overall, we present 3DVascNet, a freely available software that includes a user-friendly graphical interface for researchers with no programming experience, which will greatly facilitate the ability to study vascular networks in 3D in health and disease. Moreover, the source code of 3DVascNet is publicly available, thus it can be easily extended for the analysis of other 3D vascular networks by other users.

Transcranial volumetric imaging using a conformal ultrasound patch.

Accurate and continuous monitoring of cerebral blood flow is valuable for clinical neurocritical care and fundamental neurovascular research. Transcranial Doppler (TCD) ultrasonography is a widely used non-invasive method for evaluating cerebral blood flow1, but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording2. Here we report a conformal ultrasound patch for hands-free volumetric imaging and continuous monitoring of cerebral blood flow. The 2 MHz ultrasound waves reduce the attenuation and phase aberration caused by the skull, and the copper mesh shielding layer provides conformal contact to the skin while improving the signal-to-noise ratio by 5 dB. Ultrafast ultrasound imaging based on diverging waves can accurately render the circle of Willis in 3D and minimize human errors during examinations. Focused ultrasound waves allow the recording of blood flow spectra at selected locations continuously. The high accuracy of the conformal ultrasound patch was confirmed in comparison with a conventional TCD probe on 36 participants, showing a mean difference and standard deviation of difference as -1.51 ± 4.34 cm s-1, -0.84 ± 3.06 cm s-1 and -0.50 ± 2.55 cm s-1 for peak systolic velocity, mean flow velocity, and end diastolic velocity, respectively. The measurement success rate was 70.6%, compared with 75.3% for a conventional TCD probe. Furthermore, we demonstrate continuous blood flow spectra during different interventions and identify cascades of intracranial B waves during drowsiness within 4 h of recording.

Revealing the complexity of meniscus microvasculature through 3D visualization and analysis

Three-dimensional information is essential for a proper understanding of the healing potential of the menisci and their overall role in the knee joint. However, to date, the study of meniscal vascularity has relied primarily on two-dimensional imaging techniques. Here we present a method to elucidate the intricate 3D meniscal vascular network, revealing its spatial arrangement, connectivity and density. A polymerizing contrast agent was injected into the femoral artery of human cadaver legs, and the meniscal microvasculature was examined using micro-computed tomography at different levels of detail and resolution. The 3D vascular network was quantitatively assessed in a zone-base analysis using parameters such as diameter, length, tortuosity, and branching patterns. The results of this study revealed distinct vascular patterns within the meniscus, with the highest vascular volume found in the outer perimeniscal zone. Variations in vascular parameters were found between the different circumferential and radial meniscal zones. Moreover, through state-of-the-art 3D visualization using micro-CT, this study highlighted the importance of spatial resolution in accurately characterizing the vascular network. These findings, both from this study and from future research using this technique, improve our understanding of microvascular distribution, which may lead to improved therapeutic strategies.

A robust vessel-labeling pipeline with high tissue clearing compatibility for 3D mapping of vascular networks

The combination of vessel-labeling, tissue-clearing, and light-sheet imaging techniques provides a potent tool for accurately mapping vascular networks, enabling the assessment of vascular remodeling in vascular-related disorders. However, most vascular labeling methods face challenges such as inadequate labeling efficiency or poor compatibility with current tissue clearing technology, which significantly undermines the image quality. To address this limitation, we introduce a vessel-labeling pipeline, termed Ultralabel, which relies on a specially designed dye hydrogel containing lysine-fixable dextran and gelatins for double enhancement. Ultralabel demonstrates not only excellent vessel-labeling capability but also strong compatibility with all tissue clearing methods tested, which outperforms other vessel-labeling methods. Consequently, Ultralabel enables fine mapping of vascular networks in diverse organs, as well as multi-color labeling alongside other labeling techniques. Ultralabel should provide a robust and user-friendly method for obtaining 3D vascular networks in different biomedical applications.

Development of a 3D Vascular Network Visualization Platform for One-Dimensional Hemodynamic Simulation.

Recent advancements in computational performance and medical simulation technology have made significant strides, particularly in predictive diagnosis. This study focuses on the blood flow simulation reduced-order models, which provide swift and cost-effective solutions for complex vascular systems, positioning them as practical alternatives to 3D simulations in resource-limited medical settings. The paper introduces a visualization platform for patient-specific and image-based 1D-0D simulations. This platform covers the entire workflow, from modeling to dynamic 3D visualization of simulation results. Two case studies on, respectively, carotid stenosis and arterial remodeling demonstrate its utility in blood flow simulation applications.

Towards Realistic 3D Models of Tumor Vascular Networks.

For reliable in silico or in vitro investigations in, for example, biosensing and drug delivery applications, accurate models of tumor vascular networks down to the capillary size are essential. Compared to images acquired with conventional medical imaging techniques, digitalized histological tumor slices have a higher resolution, enabling the delineation of capillaries. Volume rendering procedures can then be used to generate a 3D model. However, the preparation of such slices leads to misalignments in relative slice orientation between consecutive slices. Thus, image registration algorithms are necessary to re-align the slices. Here, we present an algorithm for the registration and reconstruction of a vascular network from histologic slices applied to 169 tumor slices. The registration includes two steps. First, consecutive images are incrementally pre-aligned using feature- and area-based transformations. Second, using the previous transformations, parallel registration for all images is enabled. Combining intensity- and color-based thresholds along with heuristic analysis, vascular structures are segmented. A 3D interpolation technique is used for volume rendering. This results in a 3D vascular network with approximately 400-450 vessels with diameters down to 25-30 µm. A delineation of vessel structures with close distance was limited in areas of high structural density. Improvement can be achieved by using images with higher resolution and or machine learning techniques.

Direct Blood Cell Flow Imaging in Microvascular Networks.

Blood flow dynamics in microvascular networks are intimately related to the health of tissues and organs. While numerous imaging modalities and techniques have been developed to assess blood flow dynamics for various applications, their utilization has been hampered by limited imaging speed and indirect quantification of blood flow dynamics. Here, direct blood cell flow imaging (DBFI) is demonstrated that provides visualization of individual motions of blood cells over a field of 0.71mm × 1.42mm with a time resolution of 0.69ms (1450 frames s-1 ) without using any exogenous agents. DBFI enables precise dynamic analysis of blood cell flow velocities and fluxes in various vessels over a large field, from capillaries to arteries and veins, with unprecedented time resolution. Three exemplary applications of DBFI, quantification of blood flow dynamics of 3D vascular networks, analysis of heartbeat induced blood flow dynamics, and analysis of blood flow dynamics of neurovascular coupling, illustrate the potential of this new imaging technology.

Self-healing cementitious composites with a hollow vascular network created using 3D-printed sacrificial templates

Additively manufactured vascular networks have great potential for use in autonomous self-healing of cementitious composites as they potentially allow multiple healing events to take place. However, the existence of a vascular tube wall may impede with the healing efficiency if it does not rupture timely to release the healing agent. The issue of vascular material design has therefore been a major topic of research. To overcome this, dissolvable Polyvinyl Alcohol (PVA) filament is adopted in this study to fabricate the vascular networks. Fabricated networks are coated with wax, placed in cementitious mortar and removed upon hardening, thereby leaving a network of hollow channels. Different printing directions were expected to affect the dissolvability of printed structures and were therefore fabricated and tested. Different shapes (i.e., 2D and 3D) of vascular networks were printed and embedded in the cementitious mortar. Four-point bending tests and permeability tests were performed to investigate the healing efficiency. Multiple healing cycles were applied in the cracked specimens. The results show that the vertically printed PVA tubes with wax coating have good dissolution behaviour. As expected, the existence of vascular networks decreases the initial flexural strength of the specimens. In terms of healing efficiency, excellent mechanical and water tightness recovery were achieved when using epoxy resin as the healing agent. The mechanical recovery after the first healing process is higher than the following healing process. The watertightness of the cracked samples keeps decreasing with the increase of healing cycles. Specimens embedded with 3D vascular networks have higher healing potential than those utilizing 2D vascular networks.

Definition and Quantification of Three-Dimensional Imaging Targets to Phenotype Pre-Eclampsia Subtypes: An Exploratory Study

Pre-eclampsia is a severe placenta-related complication of pregnancy with limited early diagnostic and therapeutic options. Aetiological knowledge is controversial, and there is no universal consensus on what constitutes the early and late phenotypes of pre-eclampsia. Phenotyping of native placental three-dimensional (3D) morphology offers a novel approach to improve our understanding of the structural placental abnormalities in pre-eclampsia. Healthy and pre-eclamptic placental tissues were imaged with multiphoton microscopy (MPM). Imaging based on inherent signal (collagen, and cytoplasm) and fluorescent staining (nuclei, and blood vessels) enabled the visualization of placental villous tissue with subcellular resolution. Images were analysed with a combination of open source (FIJI, VMTK, Stardist, MATLAB, DBSCAN), and commercially (MATLAB) available software. Trophoblast organization, 3D-villous tree structure, syncytial knots, fibrosis, and 3D-vascular networks were identified as quantifiable imaging targets. Preliminary data indicate increased syncytial knot density with characteristic elongated shape, higher occurrence of paddle-like villous sprouts, abnormal villous volume-to-surface ratio, and decreased vascular density in pre-eclampsia compared to control placentas. The preliminary data presented indicate the potential of quantifying 3D microscopic images for identifying different morphological features and phenotyping pre-eclampsia in placental villous tissue.

Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system

Understanding the 3D cerebral vascular network is one of the pressing issues impacting the diagnostics of various systemic disorders and is helpful in clinical therapeutic strategies. Unfortunately, the existing software in the radiological workstation does not meet the expectations of radiologists who require a computerized system for detailed, quantitative analysis of the human cerebrovascular system in 3D and a standardized geometric description of its components. In this study, we show a method that uses 3D image data from magnetic resonance imaging with contrast to create a geometrical reconstruction of the vessels and a parametric description of the reconstructed segments of the vessels. First, the method isolates the vascular system using controlled morphological growing and performs skeleton extraction and optimization. Then, around the optimized skeleton branches, it creates tubular objects optimized for quality and accuracy of matching with the originally isolated vascular data. Finally, it optimizes the joints on n-furcating vessel segments. As a result, the algorithm gives a complete description of shape, position in space, position relative to other segments, and other anatomical structures of each cerebrovascular system segment. Our method is highly customizable and in principle allows reconstructing vascular structures from any 2D or 3D data. The algorithm solves shortcomings of currently available methods including failures to reconstruct the vessel mesh in the proximity of junctions and is free of mesh collisions in high curvature vessels. It also introduces a number of optimizations in the vessel skeletonization leading to a more smooth and more accurate model of the vessel network. We have tested the method on 20 datasets from the public magnetic resonance angiography image database and show that the method allows for repeatable and robust segmentation of the vessel network and allows to compute vascular lateralization indices.Graphical abstract

Angiogenesis driven extracellular matrix remodeling of 3D bioprinted vascular networks

Angiogenesis plays a pivotal role in development and tissue growth, as well as in pathological conditions such as cancer. Being able to understand the basic mechanisms involved in the vascularization of tissues and angiogenic network formation provides a window to advance the development of in vitro tissue models and enhance tissue engineering applications. In this study, we leveraged a novel microfluidic-based three dimensional (3D) bioprinting technology and alginate-collagen type I (AGC) bioink, to develop a 3D bioprinting strategy to enable the biofabrication of complex angiogenic networks within the 3D structure. These networks were comprised of simian vacuolating virus 40 (SV40) transformed adult rat brain endothelial cell (SV-ARBEC)-laden hydrogel rings. With mechanical properties relevant for vascular tissue engineering applications, these bioprinted constructs formed spontaneous vascular networks, reminiscent of anisotropic tissue-like structures, while retaining high cellular viability. The vascular network formation was accompanied by extracellular matrix (ECM) remodeling, confirming sequential SV-ARBEC mediated collagen type I fiber deposition and reorganization. Treatment with broad spectrum matrix metalloproteinase (MMP) inhibitor supressed SV-ARBEC angiogenic sprouting, highlighting requirements of ECM remodeling in angiogenic network formation. This novel 3D microfluidic bioprinting technology and biocompatible AGC hydrogel fiber rings supported robust SV-ARBEC angiogenesis and corresponding ECM remodeling, allowing us to present a strategy suitable to advancing applications in vascular research and supporting the further development of disease models, novel testing beds for drug discovery and tissue engineering applications.

In Situ Endothelialization of Free-Form 3D Network of Interconnected Tubular Channels via Interfacial Coacervation by Aqueous-in-Aqueous Embedded Bioprinting.

The challenge of bioprinting vascularized tissues is structure retention and in situ endothelialization. The issue is addressed by adopting an aqueous-in-aqueous 3D embedded bioprinting strategy, in which the interfacial coacervation of the cyto-mimic aqueous two-phase systems (ATPS) are employed for maintaining the suspending liquid architectures, and serving as filamentous scaffolds for cell attachment and growth. By incorporating endothelial cells in the ink phase of ATPS, tubular lumens enclosed by coacervated complexes of polylysine (PLL) and oxidized bacteria celluloses (oxBC) can be cellularized with a confluent endothelial layer, without any help of adhesive peptides. By applying PLL/oxBC ATPS for embedded bioprinting, free-form 3D vascular networks with in situ endothelialization of interconnected tubular lumens are achieved. This simple approach is a one-step process without any sacrificed templates and post-treatments. The resultant functional vessel networks with arbitrary complexity are suspended in liquid medium and can be conveniently handled, opening new routes for the in vitro production of thick vascularized tissues for pathological research, regeneration therapy and animal-free drug development.

Abstract C069: Using a cancer-on-a-chip approach to study the pancreatic cancer tumor microenvironment

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is overwhelmingly resistant to therapy. PDAC tumors are characterized by an immunosuppressive fibroinflammatory stroma and can be broadly classified into those with an abundance of T cells or a paucity of T cells. Our lab has previously identified several tumor cell intrinsic factors that regulate the degree of T cell infiltration and response to immunotherapy, but it remains unknown how these factors influence T cell trafficking through the vasculature to the tumor. We used in vivo models and a cancer-on-a-chip platform to examine immune-vascular crosstalk in the pancreatic tumor microenvironment (TME). We first sought to characterize the vascular landscape in PDAC. Following up from previous work in the lab, we used bulk RNA sequencing appraoches to identify a subset of hyper-vascular murine PDAC lines. Using an in vivo dextran assay, we determined that endothelial-high tumors have increased vascular perfusion. We next utilized an in vitro tube formation assay to assess if the defects seen in tumor vasculature are from tumor-cell derived factors. Endothelial cells cultured in conditioned media from endothelial-low tumors had defects in tubule formation. We further identified endothelial-low tumors have a defect in pericyte coverage of the vessel. We next utilized organ-on-a-chip devices to assess angiogenesis in PDAC. Endothelial cells and fibroblasts self-assemble into a 3D vascular network when incorporated into the microdevices. In the absence of tumor cells, mature vascular networks formed on day 4 of culture. Interestingly, vascular networks formed faster in the presence of PDAC tumor cells, suggesting a tumor derived factor enhanced angiogenesis. The cancer-on-a-chip devices are perfusable and allow for the incorporation of other TME components. Future directions, include incorporation of myeloid cells and perfusing fluorescently labeled T cells into the vascular networks and using live imaging to monitor T cell trafficking in different PDAC TME settings. Further, we will use immune checkpoint blockade and CAR T cells to identify methods to enhance tumoral T cell infiltration. Citation Format: Samantha B. Kemp, Andrei Georgescu, Jason Pitarresi, Takeshi Katsuda, Il-Kyu Kim, Dora Racca, John Tobias, Dan Huh, Ben Stanger. Using a cancer-on-a-chip approach to study the pancreatic cancer tumor microenvironment [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C069.

In vitro vascularization of hydrogel-based tissue constructs via a combined approach of cell sheet engineering and dynamic perfusion cell culture

The bioengineering of artificial tissue constructs requires special attention to their fast vascularization to provide cells with sufficient nutrients and oxygen. We addressed the challenge of in vitro vascularization by employing a combined approach of cell sheet engineering, 3D printing, and cellular self-organization in dynamic maturation culture. A confluent cell sheet of human umbilical vein endothelial cells (HUVECs) was detached from a thermoresponsive cell culture substrate and transferred onto a 3D-printed, perfusable tubular scaffold using a custom-made cell sheet rolling device. Under indirect co-culture conditions with human dermal fibroblasts (HDFs), the cell sheet-covered vessel mimic embedded in a collagen gel together with additional singularized HUVECs started sprouting into the surrounding gel, while the suspended cells around the tube self-organized and formed a dense lumen-containing 3D vascular network throughout the gel. The HDFs cultured below the HUVEC-containing cell culture insert provided angiogenic support to the HUVECs via molecular crosstalk without competing for space with the HUVECs or inducing rapid collagen matrix remodeling. The resulting vascular network remained viable under these conditions throughout the 3 week cell culture period. This static indirect co-culture setup was further transferred to dynamic flow conditions, where the medium perfusion was enabled via two independently addressable perfusion circuits equipped with two different cell culture chambers, one hosting the HDFs and the other hosting the HUVEC-laden collagen gel. Using this system, we successfully connected the collagen-embedded HUVEC culture to a dynamic medium flow, and within 1 week of the dynamic cell culture, we detected angiogenic sprouting and dense microvascular network formation via HUVEC self-organization in the hydrogel. Our approach of combining a 3D-printed and cell sheet-covered vascular precursor that retained its sprouting capacity together with the self-assembling HUVECs in a dynamic perfusion culture resulted in a vascular-like 3D network, which is a critical step toward the long-term vascularization of bioengineered in vitro tissue constructs.

Multiscale topology characterizes dynamic tumor vascular networks.

Advances in imaging techniques enable high-resolution three-dimensional (3D) visualization of vascular networks over time and reveal abnormal structural features such as twists and loops, and their quantification is an active area of research. Here, we showcase how topological data analysis, the mathematical field that studies the “shape” of data, can characterize the geometric, spatial, and temporal organization of vascular networks. We propose two topological lenses to study vasculature, which capture inherent multiscale features and vessel connectivity, and surpass the single-scale analysis of existing methods. We analyze images collected using intravital and ultramicroscopy modalities and quantify spatiotemporal variation of twists, loops, and avascular regions (voids) in 3D vascular networks. This topological approach validates and quantifies known qualitative trends such as dynamic changes in tortuosity and loops in response to antibodies that modulate vessel sprouting; furthermore, it quantifies the effect of radiotherapy on vessel architecture.

Quantification of vascular networks in photoacoustic mesoscopy

Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature. The visual or semi-quantitative 2D measurements typically applied to mesoscopic PAI data fail to capture the 3D vessel network complexity and lack robust ground truths for assessment of accuracy. Here, we developed a pipeline for quantifying 3D vascular networks captured using mesoscopic PAI and tested the preservation of blood volume and network structure with topological data analysis. Ground truth data of in silico synthetic vasculatures and a string phantom indicated that learning-based segmentation best preserves vessel diameter and blood volume at depth, while rule-based segmentation with vesselness image filtering accurately preserved network structure in superficial vessels. Segmentation of vessels in breast cancer patient-derived xenografts (PDXs) compared favourably to ex vivo immunohistochemistry. Furthermore, our findings underscore the importance of validating segmentation methods when applying mesoscopic PAI as a tool to evaluate vascular networks in vivo.

A Workflow for Three-Dimensional Reconstruction and Quantification of the Monkey Optic Nerve Head Vascular Network.

A comprehensive characterization of the three-dimensional (3D) vascular network of the optic nerve head (ONH) is critical to understanding eye physiology and pathology. Current in vivo imaging technologies, however, do not have simultaneous high spatial resolution and imaging depth to resolve the small vessels deep within the ONH. We describe a workflow for the 3D reconstruction and quantitative morphological analysis of the ONH vasculature. The vessels of a normal monkey ONH were perfusion labeled. Serial cryosections of the ONH were imaged using fluorescence microscopy (FM) and instant polarized light microscopy (IPOL) to visualize the labeled vessels and label-free collagen, respectively. The IPOL images were registered and used to form a stack of FM images from which the vessels were segmented and skeletonized to reconstruct the 3D vascular network. The network consisted of 12,966 vessel segments, 7989 branching points, and 1100 terminal points at the boundaries. For each vessel segment, we measured its length, tortuosity, inclination (θ), and polar orientation (φ). The length followed a lognormal distribution, whereas the distribution of the tortuosity followed an exponential decay. The vessels were mainly oriented toward the coronal plane (θ = 90 deg). For orientation, there were nearly as many vessels aligned circumferentially (φ = 90 deg) and radially (φ = 0 deg). Our results demonstrate the workflow for 3D eye-specific reconstruction and quantification of the monkey ONH vascular network. This is a critical first step to analyze the blood flow and oxygenation within the ONH, which will help understand the role of vascular dysfunction in glaucoma.

Histology and Vascular Architecture Study of Keloid Tissue to Outline the Possible Terminology of Keloid Skin Flaps.

Using the keloid "epidermis" to cover a wound is widely used during treatment for keloids. Many flap terminologies have been used in literature. However, the definition of the flap is not well established. Here, we refined the definition of the flap and associated terminology and explored the survival mechanism of the 'flap' through histological analysis and blood supply studying. Histology and vascular study of keloid was carried out with keloid and its surrounding normal skin tissue which were collected from keloid patients following keloid resection operations. The histological structures and thicknesses of epidermal and subepidermal of the keloids were analyzed and measured using hematoxylin & eosin (H&E) staining. Vascular density and blood perfusion in the subepidermal layer of keloids (KDS) were analyzed using CD31 immunohistochemical staining and a laser speckle contrast imaging system (LSCI), respectively. The vascular network in KDS was visualized by CD31 immunofluorescence staining and three-dimensional reconstruction. 29 pieces of keloid and its surrounding normal skin tissue sample from ten patients were collected. Keloid samples were about 2 cm wide and 5 cm long. The normal skin samples were about 2 to 3 mm in width. The thickness of epidermal layer of keloids was (136.4 ± 35.3) μm, and the thickness of epidermal layer of surrounding normal skin was (78.8 ± 13.9) μm. There was statistical thickness difference between the two layers, t(20) = 7.469, P < 0.001. The total thickness of keloid epidermal and subepidermal layers was 391.4 ± 2.3 μm. The vascular density (13.9 ± 3.4/field) and blood flow perfusion (132.7 ± 31.3) PU in KDS were greater than that of surrounding normal skin (7.8 ± 2.3/field, 73.9 ± 17.9 PU), P < 0.001. Horizontally distributed vessels with several vertical branches were observed in 3D vascular network reconstruction. The epidermal layer of keloid is thicker than that of surrounding normal skin. There is a vascular network structure under it. The vessels mainly locate at a depth of about 150 to 400 μm from the surface of keloid epidermis, randomly distribute and run parallel to the epidermis. Based on these characteristics which may ensure an adequate blood supply, we propose the concept of a "keloid subepidermal vascular network flap." This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .