Thin Vessels Research Articles

B-Flow and Contrast-Enhanced Ultrasound (CEUS) Features of Subcutaneous Masses and Nodular Lesions in Dogs.

Subcutaneous lesions in dogs are common in clinical practice. This prospective clinical study aims to compare B-flow and CEUS for the characterization of subcutaneous lesions evaluating their usefulness to distinguish benign subcutaneous lesions from malignant ones. Dogs were enrolled and ultrasound cine-loops were achieved in B-mode, Colour Doppler, Power Doppler, B-flow and CEUS. Lesions vascularisation highlighted through B-flow and CEUS were classified into five patterns: P1, absence of contrast uptake; P2, enhancement only in the peripheral area of the lesion; P3, thin (<2 mm) and few vessels (<5/field); P4, thicker (>2 mm) and more numerous vessels (>5/field); P5 enhancement with a reticular aspect and both thick and thin bands inside. Patterns highlighted with B-flow and CEUS were compared to a histological diagnosis of subcutaneous lesions. A total of 24 dogs and 30 subcutaneous nodules were included and divided into three groups: 3 non-neoplastic, 16 benign tumours and 11 malignant tumours. There was a statistically significant difference for B-flow and CEUS to differentiate benign tumours from malignant tumours. B-flow and CEUS had an excellent agreement. B-flow and CEUS displayed similar ability to evaluate different patterns and could be helpful in the evaluation of subcutaneous nodules.

Thin vessel segmentation in fundus images using attention UNet and modified Frangi filtering

Thin vessel segmentation is an active research problem, with an emphasis on finding a universal approach for different types of fundus datasets. Enhancement of the thin vessels is the first and foremost task for proper segmentation, which is proposed to be done with the total variation (TV) decomposition method with layer-selective enhancement and illumination correction. The vessel segmentation task is carried out on two fronts. For thin vessels, we propose the attention UNet backbone, and for thick vessels, the modified Frangi method is used. The method is trained and tested on HRF, CHASE_DB1, and DRIVE datasets with accuracy of 96.73%, 96.38%, and 94.97%, and sensitivity of 92.66%, 81.05%, and 87.56%, respectively. The method was cross-validated on the STARE dataset with an accuracy of 96.30% and a sensitivity of 93.69%. The sensitivity performance surpasses the state of the art.

In vivo video microscopy of the rupturing process of thin blood vessels to clarify the mechanism of bruising caused by blunt impact: an animal study

BackgroundThe thresholds of mechanical inputs for bruising caused by blunt impact are important in the fields of machine safety and forensics. However, reliable data on these thresholds remain inadequate owing to a lack of in vivo experiments, which are crucial for investigating the occurrence of bruising. Since experiments involving live human participants are limited owing to ethical concerns, finite-element method (FEM) simulations of the bruising mechanism should be used to compensate for the lack of experimental data by estimating the thresholds under various conditions, which requires clarifying the mechanism of formation of actual bruises. Therefore, this study aimed to visualize the mechanism underlying the formation of bruises caused by blunt impact to enable FEM simulations to estimate the thresholds of mechanical inputs for bruising.MethodsIn vivo microscopy of a transparent glass catfish subjected to blunt contact with an indenter was performed. The fish were anesthetized by immersing them in buffered MS-222 (75–100 mg/L) and then fixed on a subject tray. The indenter, made of transparent acrylic and having a rectangular contact area with dimensions of 1.0 mm × 1.5 mm, was loaded onto the lateral side of the caudal region of the fish. Blood vessels and surrounding tissues were examined through the transparent indenter using a microscope equipped with a video camera. The contact force was measured using a force-sensing table.ResultsOne of the processes of rupturing thin blood vessels, which are an essential component of the bruising mechanism, was observed and recorded as a movie. The soft tissue surrounding the thin blood vessel extended in a plane perpendicular to the compressive contact force. Subsequently, the thin blood vessel was pulled into a straight configuration. Next, it was stretched in the axial direction and finally ruptured.ConclusionThe results obtained indicate that the extension of the surrounding tissue in the direction perpendicular to the contact force as well as the extension of the thin blood vessels are important factors in the bruising mechanism, which must be reproduced by FEM simulation to estimate the thresholds.

Evaluation of the novel ALK5 inhibitor EW-7197 on therapeutic efficacy in renal fibrosis using a three-dimensional chip model.

EW-7197, a potent oral ALK5 inhibitor, was assessed for its impact on transforming growth factor beta 1 (TGF-β1)-induced fibrosis in a three-dimensional (3D) renal fibrosis-on-a-chip and a mouse model. The evaluation included tubular epithelial-mesenchymal transition, angiogenesis, and inflammatory cytokine expression. In a 3D renal fibrosis-on-a-chip model, three cell types (kidney fibroblasts, human proximal tubular cell line, and human umbilical vein endothelial cells) were cultured and treated with TGF-β1 and EW-7197. Expression of alpha smooth muscle actin (α-SMA) and keratin 8 (KRT-8) was assessed, angiogenesis was observed via confocal microscopy, and cytokine levels were measured using real-time polymerase chain reaction, immunoassay, and enzyme-linked immunosorbent assay. In a cisplatin-induced renal fibrosis mouse model, blood urea nitrogen levels, TGF-β, and Smad 2/3 were determined, and renal fibrosis was assessed with Masson's trichrome stain. The α-SMA expression was significantly lower in the EW-7197 group than in the TGF-β fibrosis group. TGF-β decreased the expression of the epithelial marker KRT-8, an effect that was reversed by EW-7197 and SB431542. In the TGF-β-induced fibrosis model, the length of the thick vessels was reduced, and the diameter of both thick and thin vessels was decreased, but EW-7197 reversed these effects. EW-7197 significantly reduced the messenger RNA expression of TGF-β and increased the levels of vascular endothelial growth factor receptor 2, interleukin (IL)-10, and IL-6. EW-7197 reduced the levels of secretory cytokines TGF-β1, TGF-β3, IL-1β. In the cisplatin-induced renal fibrosis mouse model, EW-7197 reduced renal fibrosis by down-regulating TGF-β signaling. EW-7197 attenuated the TGF-β1-induced fibrotic cellular response in the 3D chip model and animal model. These findings indicate the potential effect of EW-7197 in attenuating renal fibrosis.

Spatial attention U-Net model with Harris hawks optimization for retinal blood vessel and optic disc segmentation in fundus images.

The state of the human eye's blood vessels is a crucial aspect in the diagnosis of ophthalmological illnesses. For many computer-aided diagnostic systems, precise retinal vessel segmentation is an essential job. However, it remains a difficult task due to the intricate vascular system of the eye. Although many different vascular segmentation techniques have already been presented, additional study is still required to address the problem of inadequate segmentation of thin and tiny vessels. In this work, we introduce the Spatial Attention U-Net (SAU-Net) modelwith harris hawks' optimization (HHO), a lightweight network that can be applied as a data augmentation technique to improve the efficiency of the existingannotated samples without the need of thousands of training instances for Retinal Blood Vessel and Optic Disc Segmentation. The SAU-Net-HHO implementation uses a spatially inferred attention map multiplied by the input feature map for adaptive feature enhancement. U-Net convolutional blocks have been replaced with structured dropout blocks in the proposed network to prevent overfitting. Data from both vascular extraction (DRIVE) and structured analysis of the retina (STARE) are used to evaluate SAU-Net-HHO performance. The results show that the proposed SAU-Net-HHO performs well on both datasets. Analysing the obtained results, an average of 98.5% accuracy and Specificity 96.7% was achieved for DRIVE dataset and 97.8% accuracy and specificity 94.5% for STARE dataset. The proposed method yields numerical results with average values that are on par with those of state-of-the-art methods. Visual inspection has revealed that the suggested method can segment thin and tiny vessels with greater accuracy than previous methods. It also demonstrates its potential for real-life clinical application.

CAS‐GAN: A Novel Generative Adversarial Network‐Based Architecture for Coronary Artery Segmentation

ABSTRACTAccurate and automated segmentation of x‐ray coronary angiography (XRCA) is crucial for both diagnosing and treating coronary artery diseases. Despite the outstanding results achieved by deep learning (DL)‐based methods in this area, this task remains challenging due to several factors such as poor image quality, the presence of motion artifacts, and inherent variability in vessel structure sizes. To address this challenge, this paper introduces a novel GAN‐based architecture for coronary artery segmentation using XRCA images. This architecture includes a novel U‐Net variant with two types of self‐attention blocks in the generator segment. An auxiliary path connects the attention block and the prediction block to enhance feature generalization, improving vessel structure delineation, especially thin vessels in low‐contrast regions. In parallel, the discriminator network employs a residual CNN with similar attention blocks for balanced performance and improved predictive capabilities. With a streamlined 6.74 M parameters, the resulting architecture surpasses existing methods in efficiency. We assess its efficacy on three coronary artery datasets: our private “CORONAR,” and the public “DCA1” and “CHUAC” datasets. Empirical results showcase our model's superiority across these datasets, utilizing both original and preprocessed images. Notably, our proposed architecture achieves the highest F1‐score of 0.7972 for the CHUAC dataset, 0.8245 for the DCA1 dataset, and 0.8333 for the CORONAR dataset.

Skeleton-guided multi-scale dual-coordinate attention aggregation network for retinal blood vessel segmentation

Deep learning plays a pivotal role in retinal blood vessel segmentation for medical diagnosis. Despite their significant efficacy, these techniques face two major challenges. Firstly, they often neglect the severe class imbalance in fundus images, where thin vessels in the foreground are proportionally minimal. Secondly, they are susceptible to poor image quality and blurred vessel edges, resulting in discontinuities or breaks in vascular structures. In response, this paper proposes the Skeleton-guided Multi-scale Dual-coordinate Attention Aggregation (SMDAA) network for retinal vessel segmentation. SMDAA comprises three innovative modules: Dual-coordinate Attention (DCA), Unbalanced Pixel Amplifier (UPA), and Vessel Skeleton Guidance (VSG). DCA, integrating Multi-scale Coordinate Feature Aggregation (MCFA) and Scale Coordinate Attention Decoding (SCAD), meticulously analyzes vessel structures across various scales, adept at capturing intricate details, thereby significantly enhancing segmentation accuracy. To address class imbalance, we introduce UPA, dynamically allocating more attention to misclassified pixels, ensuring precise extraction of thin and small blood vessels. Moreover, to preserve vessel structure continuity, we integrate vessel anatomy and develop the VSG module to connect fragmented vessel segments. Additionally, a Feature-level Contrast (FCL) loss is introduced to capture subtle nuances within the same category, enhancing the fidelity of retinal blood vessel segmentation. Extensive experiments on three public datasets (DRIVE, STARE, and CHASE_DB1) demonstrate superior performance in comparison to current methods. The code is available at https://github.com/wangwxr/SMDAA_NET.

Assessment of retinal blood vessel segmentation using U-Net model: A deep learning approach

Segmentation of retinal blood vessels from fundus images is vital to assist ophthalmologists in diagnosing different eye diseases like Arteriosclerosis, Glaucoma, Diabetic Retinopathy, Hypertension, and Choroidal Neovascularization. Accurate detection and timely treatment of these eye diseases can prevent irreversible impairment of vision. In computer-aided automatic diagnosis, Preprocessing of fundus images is necessary to reduce the uneven retinal image illumination, as well as the undesirable effect arising from the contrast differences between the retinal blood vessels and the background. In this study, the Hessian-based Frangi vesselness filter is proposed to enhance vasculature in fundus images. This approach seeks to extract thin vessels to diminish the intensity disparity between thick and thin vessels. Its accuracy has never, however, been thoroughly evaluated. To verify the effectiveness of the suggested filter for boosting vessel-like structures in the fundus images, an experimental approach is presented in this paper. The ability of the filter to improve vessel-like structures in fundus images is validated, and an experimental technique for evaluating filter performance using the U-Net model is described. Five quantitative performances, namely Accuracy, Recall, Specificity, Precision, and F1 Score measures on the DRIVE, HRF, DIARETDB1, DIARETDB0, CHASEDB1, ORIGA, DRISHTI GS, DRIONS DB, FIRE, and FIOT datasets, were evaluated and quantified in these experiments to validate the efficacy of the proposed filter. The results demonstrated a noteworthy improvement by achieving an Accuracy of 99.79 %, 99.84 %, 99.64 %, 99.72 %, 99.57 %, 99.48 %, 99.77 %, 99.05 %, 99.93 %, and 99.40 % respectively. Empirical evidence supports the advantages of this approach.

A novel compound heterozygous variant of MYO7A in Usher syndrome type 1

Usher syndrome (USH) is a recessive genetic disorder manifested by congenital sensorineural hearing loss and progressive retinitis pigmentosa, which leads to audiovisual impairment. We report a patient with Usher syndrome type 1 with new compound heterozygous MYO7A variants. A total of four members from the USH family were included. Medical history and retinal examinations were taken and genomic DNA from peripheral blood was extracted in the proband and other members. 381 retinal disease-associated genes were screened using targeted sequence capture array technology and Sanger sequencing was used to confirm the screening results. Scanning laser ophthalmoscope showed bone spicule pigmentary deposits in the mid-peripheral retina and whitish and thin retinal blood vessels especially in the arterioles. Optical coherence tomography showed that the centrality of the macular ellipsoid band disappeared in both eyes, and only remained near the fovea. Visual field examination showed a progressive loss of the visual field in a concentric pattern in both eyes. The electroretinography showed a significant decrease in the amplitudes of a- and b-waves in the scotopic and photopic condition. DNA sequencing identified the compound heterozygous variants including c.1003+1G > A: p. (?) and c.5957_5958del: p.G1987Lfs*50 of MYO7A, with the latter being novel. In this study, we found a novel compound heterozygous variant in MYO7A, which enriched the mutation spectrum and expanded our understanding of the heterogeneity of phenotype and genotype of Usher syndrome type 1.

Microglia in the hypothalamic paraventricular nucleus sense hemodynamic disturbance and promote sympathetic excitation in hypertension

Hypertension is usually accompanied by elevated sympathetic tonicity, but how sympathetic hyperactivity is triggered is not clear. Recent advances revealed that microglia-centered neuroinflammation contributes to sympathetic excitation in hypertension. In this study, we performed a temporospatial analysis of microglia at both morphological and transcriptomic levels and found that microglia in the hypothalamic paraventricular nucleus (PVN), a sympathetic center, were early responders to hypertensive challenges. Vasculature analyses revealed that the PVN was characterized by high capillary density, thin vessel diameter, and complex vascular topology relative to other brain regions. As such, the PVN was susceptible to the penetration of ATP released from the vasculature in response to hemodynamic disturbance after blood pressure increase. Mechanistically, ATP ligation to microglial P2Y12 receptor was responsible for microglial inflammatory activation and the eventual sympathetic overflow. Together, these findings identified a distinct vasculature pattern rendering vulnerability of PVN pre-sympathetic neurons to hypertension-associated microglia-mediated inflammatory insults.

Efficacy of Cisplatin-CXCR4 Antagonist Combination Therapy in Oral Cancer.

Cisplatin is a platinum-based compound that is widely used for treating inoperable oral squamous cell carcinoma (OSCC) in Japan; however, resistance to cisplatin presents a challenge and innovative approaches are required. We aimed to investigate the therapeutic potential of targeting the chemokine receptor CXCR4, which is involved in angiogenesis and tumor progression, using the CXCR4 inhibitor AMD3100, in combination with cisplatin. AMD3100 induced necrosis and bleeding in OSCC xenografts by inhibiting angiogenesis. We investigated the combined ability of AMD3100 plus cisplatin to enhance the antitumor effect in cisplatin-resistant OSCC. An MTS assay identified HSC-2 cells as cisplatin-resistant cells in vitro. Mice treated with the cisplatin-AMD combination exhibited the most significant reduction in tumor volume, accompanied by extensive hemorrhage and necrosis. Histological examination indicated thin and short tumor vessels in the AMD and cisplatin-AMD groups. These results indicated that cisplatin and AMD3100 had synergistic antitumor effects, highlighting their potential for vascular therapy of refractory OSCC. Antitumor vascular therapy using cisplatin combined with a CXCR4 inhibitor provides a novel strategy for addressing cisplatin-resistant OSCC.

A Microvascular Segmentation Network Based on Pyramidal Attention Mechanism.

The precise segmentation of retinal vasculature is crucial for the early screening of various eye diseases, such as diabetic retinopathy and hypertensive retinopathy. Given the complex and variable overall structure of retinal vessels and their delicate, minute local features, the accurate extraction of fine vessels and edge pixels remains a technical challenge in the current research. To enhance the ability to extract thin vessels, this paper incorporates a pyramid channel attention module into a U-shaped network. This allows for more effective capture of information at different levels and increased attention to vessel-related channels, thereby improving model performance. Simultaneously, to prevent overfitting, this paper optimizes the standard convolutional block in the U-Net with the pre-activated residual discard convolution block, thus improving the model's generalization ability. The model is evaluated on three benchmark retinal datasets: DRIVE, CHASE_DB1, and STARE. Experimental results demonstrate that, compared to the baseline model, the proposed model achieves improvements in sensitivity (Sen) scores of 7.12%, 9.65%, and 5.36% on these three datasets, respectively, proving its strong ability to extract fine vessels.

PVN predisposes microglia to be susceptible to hemodynamic signals and promote sympathetic excitation in hypertension

Hypertension is usually accompanied with an elevated sympathetic tonicity, but how sympathetic hyperactivity is triggered is not fully understood. Recent advances reveal that microglia-centered neuroinflammation contributes to sympathetic excitation in hypertension. In this study, we performed a temporospatial analysis of microglia at both morphological and transcriptomic levels, and found that microglia in the hypothalamic paraventricular nucleus (PVN) were early responders to hypertensive challenges. PVN is the central hub for maintaining cardiovascular function via regulation of fluid balance and sympathetic outflow. Comprehensive vasculature analyses unveiled that PVN was characterized by high capillary density, thin vessel diameter, and complex vascular topology among brain regions. As such, PVN is susceptible to the penetration of ATP released from the vasculature in response to hemodynamic disturbance after blood pressure increase. ATP ligation to microglial P2Y12 receptor is responsible for the microglial accumulation and activation in the PVN. Both pharmacological blockade and genetic ablation of microglial P2Y12 could substantially restrain blood pressure increase under hypertensive challenge. Together, these findings disclose that a unique vasculature pattern results in the vulnerability of PVN pre-sympathetic neurons to hypertension-associated inflammatory insults, which is mediated by microglia. NSFC82170441. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

PSO-HRVSO: Segmentation of Retinal Vessels Through Homomorphic Filtering Enhanced by PSO Optimization

The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust vessel segmentation, addressing prevalent challenges identified in existing literature. The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging PSO optimization. The main processing phase is bifurcated into two configurations. Initially, thick vessels are segmented utilizing an optimized top-hat approach, homo-morphic filtering, and median filter. Subsequently, the second configuration targets thin vessel segmentation, employing the optimized top-hat method, homomorphic filtering, and matched filter. Lastly, morphological image operations are conducted during the post-processing stage. The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868, 0.8576, and 0.9831 for the STARE dataset, respectively. The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when compared to current methods. Moreover, it sur-passes all leading unsupervised methods in terms of specificity and accuracy. Additionally, it outperforms the majority of state-of-the-art supervised methods without incurring the computational costs associated with such algorithms. Detailed visual analysis reveals that the PSO-HRVSO approach enables a more precise segmentation of thin vessels compared to alternative procedures.

Cross-patch feature interactive net with edge refinement for retinal vessel segmentation

Retinal vessel segmentation based on deep learning is an important auxiliary method for assisting clinical doctors in diagnosing retinal diseases. However, existing methods often produce mis-segmentation when dealing with low contrast images and thin blood vessels, which affects the continuity and integrity of the vessel skeleton. In addition, existing deep learning methods tend to lose a lot of detailed information during training, which affects the accuracy of segmentation. To address these issues, we propose a novel dual-decoder based Cross-patch Feature Interactive Net with Edge Refinement (CFI-Net) for end-to-end retinal vessel segmentation. In the encoder part, a joint refinement down-sampling method (JRDM) is proposed to compress feature information in the process of reducing image size, so as to reduce the loss of thin vessels and vessel edge information during the encoding process. In the decoder part, we adopt a dual-path model based on edge detection, and propose a Cross-patch Interactive Attention Mechanism (CIAM) in the main path to enhancing multi-scale spatial channel features and transferring cross-spatial information. Consequently, it improve the network’s ability to segment complete and continuous vessel skeletons, reducing vessel segmentation fractures. Finally, the Adaptive Spatial Context Guide Method (ASCGM) is proposed to fuse the prediction results of the two decoder paths, which enhances segmentation details while removing part of the background noise. We evaluated our model on two retinal image datasets and one coronary angiography dataset, achieving outstanding performance in segmentation comprehensive assessment metrics such as AUC and CAL. The experimental results showed that the proposed CFI-Net has superior segmentation performance compared with other existing methods, especially for thin vessels and vessel edges. The code is available at https://github.com/kita0420/CFI-Net.

Performance evaluation of commercial and non-commercial shear wave elastography implementations for vascular applications

BackgroundShear wave elastography (SWE) is mainly used for stiffness estimation of large, homogeneous tissues, such as the liver and breasts. However, little is known about its accuracy and applicability in thin (∼0.5–2 mm) vessel walls. To identify possible performance differences among vendors, we quantified differences in measured wave velocities obtained by commercial SWE implementations of various vendors over different imaging depths in a vessel-mimicking phantom. For reference, we measured SWE values in the cylindrical inclusions and homogeneous background of a commercial SWE phantom. Additionally, we compared the accuracy between a research implementation and the commercially available clinical SWE on an Aixplorer ultrasound system in phantoms and in vivo in patients. MethodsSWE measurements were performed over varying depths (0–35 mm) using three ultrasound machines with four ultrasound probes in the homogeneous 20 kPa background and cylindrical targets of 10, 40, and 60 kPa of a multi-purpose phantom (CIRS-040GSE) and in the anterior and posterior wall of a homogeneous polyvinyl alcohol vessel-mimicking phantom. These phantom data, along with in vivo SWE data of carotid arteries in 23 patients with a (prior) head and neck neoplasm, were also acquired in the research and clinical mode of the Aixplorer ultrasound machine. Machine-specific estimated phantom stiffness values (CIRS phantom) or wave velocities (vessel phantom) over all depths were visualized, and the relative error to the reference values and inter-frame variability (interquartile range/median) were calculated. Correlations between SWE values and target/vessel wall depth were explored in phantoms and in vivo using Spearman’s correlations. Differences in wave velocities between the anterior and posterior arterial wall were assessed with Wilcoxon signed-rank tests. Intra-class correlation coefficients were calculated for a sample of ten patients as a measure of intra- and interobserver reproducibility of SWE analyses in research and clinical mode. ResultsThere was a high variability in obtained SWE values among ultrasound machines, probes, and, in some cases, with depth. Compared to the homogeneous CIRS-background, this variation was more pronounced for the inclusions and the vessel-mimicking phantom. Furthermore, higher stiffnesses were generally underestimated. In the vessel-mimicking phantom, anterior wave velocities were (incorrectly) higher than posterior wave velocities (3.4–5.6 m/s versus 2.9–5.9 m/s, p ≤ 0.005 for 3/4 probes) and remarkably correlated with measurement depth for most machines (Spearman’s ρ = -0.873–0.969, p < 0.001 for 3/4 probes). In the Aixplorer’s research mode, this difference was smaller (3.3–3.9 m/s versus 3.2–3.6 m/s, p = 0.005) and values did not correlate with measurement depth (Spearman’s ρ = 0.039–0.659, p ≥ 0.002). In vivo, wave velocities were higher in the posterior than the anterior vessel wall in research (left p = 0.001, right p < 0.001) but not in clinical mode (left: p = 0.114, right: p = 0.483). Yet, wave velocities correlated with vessel wall depth in clinical (Spearman’s ρ = 0.574–0.698, p < 0.001) but not in research mode (Spearman’s ρ = -0.080–0.466, p ≥ 0.003). ConclusionsWe observed more variation in SWE values among ultrasound machines and probes in tissue with high stiffness and thin-walled geometry than in low stiffness, homogeneous tissue. Together with a depth-correlation in some machines, where carotid arteries have a fixed location, this calls for caution in interpreting SWE results in clinical practice for vascular applications.

Automatic 3D coronary artery segmentation based on local region active contour model.

Segmentation of coronary arteries in computed tomography angiography (CTA) images plays a key role in the diagnosis and treatment of coronary-related diseases. However, manually analyzing the large amount of data is time-consuming, and interpreting this data requires the prior knowledge and expertise of radiologists. Therefore, an automatic method is needed to separate coronary arteries from a given CTA dataset. Firstly, an anisotropic diffusion filter was employed to smooth the noise while preserving the vessel boundaries. The coronary skeleton was then extracted using a two-step process based on the intensity of the coronary. In the first step, the thick vessel skeleton was extracted by clustering, improved vesselness filtering and region growing, while in the second step, the thin vessel skeleton was extracted by the height ridge traversal method guided by the cylindrical model. Next, the vesselness measure, representing vessel a priori information, was incorporated into the local region active contour model based on the vessel geometry. Finally, the initial contour of the active contour model was generated using the coronary artery skeleton for effective segmentation of the three-dimensional (3D) coronary arteries. Experimental results on chest CTA images show that the method is able to segment coronary arteries effectively with an average precision, recall and dice similarity coefficient (DSC) of 86.64%, 91.26% and 79.13%, respectively, and has a good performance in thin vessel extraction. The method does not require manual selection of vessel seeds or setting of initial contours, and allows for the extraction of a successful coronary artery skeleton and eventual effective segmentation of the coronary arteries.

Differences Between Edges and Centers of Melanomas Using Vibrational Optical Coherence Tomography: The Effects of Critical Lesion Size

Vibrational Optical Coherence Tomography (VOCT) is a new technique to noninvasively characterize skin cancers. We have previously shown that skin cancers exhibit new mechanovibrational peaks at 80, 130, and 250-260 Hz. The 80 Hz peak appears to represent new cells including Cancer Associated Fibroblasts (CAFs) while the new 130 and 250-260 Hz peaks reflect thin blood vessels and fibrotic tissue, respectively. Epithelial cell derived cancers are characterized by the formation of CAFs that are stiffer than normal fibroblasts, induce deposition of new thin vasomimictry vessels, and influence fibrotic tissue formation by expression of lysyl oxidases. The results of this study suggest that small lesions less than 0.13 mm in diameter are found in clear margins of skin cancers while lesions greater than 0.21 mm in diameter are characteristic of cancerous tissue based on histopathology. The results of this study suggest that early detection of MEL lesions as small as 0.06 mm can be achieved noninvasively using VOCT. VOCT may provide a method of screening patients for skin cancer in remote areas where Dermatologists are unavailable through use of telemedicine.

A retinal vessel segmentation network with multiple-dimension attention and adaptive feature fusion

The incidence of blinding eye diseases is highly correlated with changes in retinal morphology, and is clinically detected by segmenting retinal structures in fundus images. However, some existing methods have limitations in accurately segmenting thin vessels. In recent years, deep learning has made a splash in the medical image segmentation, but the lack of edge information representation due to repetitive convolution and pooling, limits the final segmentation accuracy. To this end, this paper proposes a pixel-level retinal vessel segmentation network with multiple-dimension attention and adaptive feature fusion. Here, a multiple dimension attention enhancement (MDAE) block is proposed to acquire more local edge information. Meanwhile, a deep guidance fusion (DGF) block and a cross-pooling semantic enhancement (CPSE) block are proposed simultaneously to acquire more global contexts. Further, the predictions of different decoding stages are learned and aggregated by an adaptive weight learner (AWL) unit to obtain the best weights for effective feature fusion. The experimental results on three public fundus image datasets show that proposed network could effectively enhance the segmentation performance on retinal blood vessels. In particular, the proposed method achieves AUC of 98.30%, 98.75%, and 98.71% on the DRIVE, CHASE_DB1, and STARE datasets, respectively, while the F1 score on all three datasets exceeded 83%. The source code of the proposed model is available at https://github.com/gegao310/VesselSeg-Pytorch-master.

WSA-MP-Net: Weak-signal-attention and multi-scale perception network for microvascular extraction in optical-resolution photoacoustic microcopy

The unique advantage of optical-resolution photoacoustic microscopy (OR-PAM) is its ability to achieve high-resolution microvascular imaging without exogenous agents. This ability has excellent potential in the study of tissue microcirculation. However, tracing and monitoring microvascular morphology and hemodynamics in tissues is challenging because the segmentation of microvascular in OR-PAM images is complex due to the high density, structure complexity, and low contrast of vascular structures. Various microvasculature extraction techniques have been developed over the years but have many limitations: they cannot consider both thick and thin blood vessel segmentation simultaneously, they cannot address incompleteness and discontinuity in microvasculature, there is a lack of open-access datasets for DL-based algorithms. We have developed a novel segmentation approach to extract vascularity in OR-PAM images using a deep learning network incorporating a weak signal attention mechanism and multi-scale perception (WSA-MP-Net) model. The proposed WSA network focuses on weak and tiny vessels, while the MP module extracts features from different vessel sizes. In addition, Hessian-matrix enhancement is incorporated into the pre-and post-processing of the input and output data of the network to enhance vessel continuity. We constructed normal vessel (NV-ORPAM, 660 data pairs) and tumor vessel (TV-ORPAM, 1168 data pairs) datasets to verify the performance of the proposed method. We developed a semi-automatic annotation algorithm to obtain the ground truth for our network optimization. We applied our optimized model successfully to monitor glioma angiogenesis in mouse brains, thus demonstrating the feasibility and excellent generalization ability of our model. Compared to previous works, our proposed WSA-MP-Net extracts a significant number of microvascular while maintaining vessel continuity and signal fidelity. In quantitative analysis, the indicator values of our method improved by about 1.3% to 25.9%. We believe our proposed approach provides a promising way to extract a complete and continuous microvascular network of OR-PAM and enables its use in many microvascular-related biological studies and medical diagnoses.