Vascular Segments Research Articles

Virtual Monoenergetic Imaging of Thoracoabdominal Computed Tomography Angiography on Photon-Counting Detector Computertomography: Assessment of Image Quality and Leveraging Low-keV Series for Salvaging Suboptimal Contrast Acquisitions

Background: The aim of this study was to assess the possibility of image improvement of ECG-gated, high-pitch computed tomography angiography (CTA) of the thoracoabdominal aorta before transaortic valve replacement (TAVR) on a novel dual-source photon-counting detector CT (PCD-CT) in the setting of suboptimal low-contrast attenuation. Methods: Continuously examined patients who underwent an ECG-gated, high-pitch CTA of the aorta on a PCD-CT with a contrast decrease of at least 50% between the ascending aorta and the common femoral arteries (CFA) were included. Patient characteristics were documented. Virtual monoenergetic imaging (VMI) reconstructions with three keV settings were generated. CT values and noise were measured for five vascular segments of the aorta and the CFA. Signal-to-noise (SNR) and contrast-to-noise ratios (CNR) were calculated. Two independent board-certified radiologists rated the images with the focus on vascular attenuation, vessel sharpness, and image quality using a 5-point Likert scale. Results: Fifty-five patients (mean age 77.4 ± 8.5 years; 15 women) were included. The SNR was significantly higher at 40 and 45 keV VMI compared to reference 70 keV (p < 0.001 and p = 0.005, respectively). The same was shown for the CNR (p < 0.001 and p = 0.0049, respectively). Subjective image evaluation showed a significant increase in vessel attenuation in the lower keV reconstructions, while the overall image quality decreased only slightly. Furthermore, 50% (8/16) of primarily non-diagnostic scans were considered diagnostic when using low-keV reconstructions (p > 0.05). Conclusions: ECG-gated CTA of the aorta in high-pitch mode on PCD-CT with suboptimal contrast enhancement at the level of the CFA can be salvaged by using low-keV VMI. This implies the possibility of radiation dose reduction by eliminating the need for repeat scans.

Fundus Vascular Segmentation Based on Data Enhancement and Invariant Feature Extraction

Deep neural networks have emerged as the predominant method for medical image segmentation, owing to their robust feature learning capabilities, enabling accurate automatic segmentation of target structures within intricate medical images. Fundus images, being crucial in diagnosing ophthalmic diseases, underscore the importance of effective segmentation techniques. However, fundus vascular images pose challenges due to their high complexity and subtle individual differences, necessitating improvement in existing segmentation methodologies for enhanced disease classification accuracy. This paper introduces a fundus blood vessel segmentation model, employing data augmentation and invariant feature extraction, to systematically tackle the core challenges in medical image processing, particularly in fundus blood vessel segmentation. These challenges include limited source domain samples and inadequate model domain generalization. The model adopts a dual-dimensional strategy. Firstly, it delves into data augmentation technology to enhance the diversity and representativeness of samples within the finite source domain. This is achieved through an image enhancement module based on Fourier transform, mitigating the impact of data scarcity on model training effectiveness. Secondly, the research focuses on interdomain invariant feature extraction, aiming to extract feature representations that consistently characterize fundus blood vessel structure and pathology across different data distributions, thereby enhancing model generalization performance in unfamiliar domains. Specifically, the paper designs a fundus image enhancement module based on Fourier transform in the data augmentation dimension. In the feature extraction dimension, it proposes a normalization module based on uncertainty theory, departing from traditional normalization methods. Experimental results demonstrate the efficacy of the proposed method, showcasing superior generalization performance compared to existing techniques in retinal blood vessel and OD/OC segmentation tasks. Experience validates the model-agnostic nature of the learned strategy, indicating its potential for seamless transferability to other models, thereby offering robust support for advancing medical image segmentation research and applications.

Reliability of 4D Flow MRI for Investigation of Fetal Cardiovascular Hemodynamics in the Third Trimester.

Purpose To provide reference values for four-dimensional (4D) flow MRI in healthy fetuses and evaluate reliability of fetal 4D flow MRI hemodynamics in third trimester fetuses with normal cardiovascular development or suspected coarctation of the aorta (CoA). Materials and Methods Pregnant patients with healthy fetuses or fetuses with echocardiographic concern for CoA were prospectively recruited between May 2021 and October 2023. Doppler US-gated fetal 4D flow MRI was performed at 3 T. Repeated 4D flow (time permitting) and two-dimensional (2D) phase contrast (PC) MRI data were acquired. Net flow was quantified, and the reliability of 4D flow measurement was evaluated by using precision across adjacent measurement planes, internal consistency based on conservation of mass, comparison of net flow from 4D flow MRI versus 2D PC MRI, and repeatability of 4D flow from separate acquisitions. Results Data were obtained in 34 pregnant participants (mean maternal age, 33 years ± 5 [SD]; mean gestational age, 35 weeks ± 2; n = 22 healthy fetuses and 12 fetuses with suspected CoA). Precision was high across all vascular segments (mean within-subject coefficient of variation = 7%). For mass conservation, there was an average difference of 19% ± 12 between ductus arteriosus plus isthmus flow versus descending aorta flow (r = 0.76). Net flow measured with 4D flow MRI correlated with that measured with 2D PC MRI (r = 0.51) but was underestimated relative to 2D PC MRI by approximately 34%. Hemodynamic parameters quantified from repeated 4D flow acquisitions had good agreement, with an intraclass correlation coefficient of 0.94 between test and retest data. Conclusion Hemodynamic measurements derived from fetal 4D flow MRI were reliable, showing good internal consistency, precision, and repeatability; however, as expected, 4D flow MRI underestimated absolute blood flow relative to 2D PC MRI. Keywords: Fetal MRI, Cardiac, Aorta, Hemodynamics/Flow Dynamics, Pulmonary Arteries Supplemental material is available for this article. © RSNA, 2024.

A semi-automatic cardiovascular annotation and quantification toolbox utilizing prior knowledge-guided feature learning

Annotation and quantification of cardiovascular in intravascular ultrasound (IVUS) images are of great significance in assisting the clinical diagnosis and treatment of coronary heart disease. However, it is time-consuming and expensive for cardiologists to manually annotate cardiovascular and calculate parameters in IVUS images using different software during the diagnosis. In this paper, we propose an IVUS analysis toolbox (SaCAQ) for semi-automatic cardiovascular annotation and quantification via prior knowledge-guided feature learning. The SaCAQ consists of two main modules: (1) a semi-automatic cardiovascular annotation module and (2) a cardiovascular quantification module. The semi-automatic cardiovascular annotation module can accurately segment the vascular wall by a IVUS images segmentation model (ISM-IVUS). The ISM-IVUS first extracts vascular morphological features and pixel intensity change features for the complex image segmentation by the expert prior knowledge feature extraction mechanism. Secondly, vascular wall is accurately segmented by the multi-scale feature extraction of attention U-Net. Moreover, the segmentation result checking mechanism is applied to verify the automatic segmentation result, which enhances the reliability of SaCAQ. Plaque regions of interest to doctors are detected by the rule-based plaque detection method, which can assist in the quantification task. Finally, image measurement methods and medically recognized calculation formulas are used in the cardiovascular quantification module to obtain clinical parameters, which significantly improves the confidence of the results. The SaCAQ is verified on our own dataset containing 5242 IVUS slice images and 383 sets of parameters. The experiments demonstrate that the SaCAQ achieves high accuracy of vascular wall segmentation: mIoU of 86.16%, Dice coefficient of 93.43%, and Hausdorff distance of 0.2171 mm. Moreover, ISM-IVUS has good segmentation speed and robustness. The calculated parameters show good correlation and p-values. All of these results indicate that the proposed SaCAQ provides an efficient, accurate, and reliable toolbox for clinical diagnosis of cardiovascular disease. The SaCAQ and user manual can be publicly available from https://github.com/zHwiz/SaCAQ.

The Transorbital Approach to the Internal Carotid and Middle Cerebral Arteries. A Dissection Study towards Targeted Access Aneurysm Clipping

BackgroundThe application of the transorbital approach (TO) for vascular lesions has been scarcely explored. In this anatomical experience, we examine the carotid and middle cerebral arteries from the TO perspective and investigate the feasibility for vascular clipping in a pseudo-vascularised model. MethodsThree fixed human cadaveric specimens (six sides) were used for dissection. A sequential TO approach: 1) conventional lateral orbital craniectomy; 2) lateral orbital rim removal; 3) anterior clinoidectomy. The clinoidal, ophthalmic and communicating carotid segments, branches, related cranial nerves and dural attachments were examined. Comparison between the three variations of bone removal determined the minimal necessary steps for each vascular segment. An additional fresh pseudo-vascularised specimen (two sides) was used to test surgical feasibility of clipping. ResultsThe TO approach allowed to expose the carotid C4 to bifurcation. Clinoidectomy and dural rings opening exposed lateral and dorsal aspects of C5/C6 and the ophthalmic, but not medial or ventral aspects, neither the superior hypophyseal artery nor the carotid cave. Posterior communicating artery could be followed from origin to end. The anterior choroidal origin was seen but disappeared behind the uncus. The carotid bifurcation was visible, M1 could be followed to its bifurcation, but A1 lied deep within the field. The corridor permitted arachnoidal dissection, vessel manipulation and clipping of exposed segments. ConclusionThe TO approach provides anatomical access to lateral and dorsal carotid siphon, complete posterior communicating and proximal middle cerebral artery. This preliminary experience suggests potential application in the elective treatment of paraclinoid and posterior communicating aneurysms.

Suspected corneal metastasis in a case of nasopharyngeal carcinoma

A 13-year-old girl child undergoing chemotherapy for the terminal stage of undifferentiated metastatic nasopharyngeal carcinoma presented to us with a complaint of poor vision in the left eye (LE). There were no complaints of pain, photophobia, or watering. On examination, there was asymmetric optic disc pallor in both eyes with a patch of corneal infiltration in the LE that was difficult to attribute to any obvious common pathology. The richly vascular posterior segment is a well-known target of ocular metastasis for several malignancies. Corneal involvement, if it occurs, is through local spread. However in this case, in the absence of any other factors to account for this corneal infiltration along with presence of adjacent corneal neovascularization, corneal metastasis is highly suspected. Unfortunately, histopathological evidence could not be collected due to the poor general health of the patient, who did not turn up for a follow-up.

Transformer-Enhanced Retinal Vessel Segmentation for Diabetic Retinopathy Detection Using Attention Mechanisms and Multi-Scale Fusion

Eye health has become a significant concern in recent years, given the rising prevalence of visual impairment resulting from various eye disorders and related factors. Global surveys suggest that approximately 2.2 billion individuals are visually impaired, with at least 1 billion affected by treatable diseases or ailments. Early detection, treatment, and screening for fundus diseases are crucial in addressing these challenges. In this study, we propose a novel segmentation model for retinal vascular delineation aimed at diagnosing diabetic retinopathy. The model integrates CBAM (Channel-Attention and Spatial-Attention) for enhanced feature representation, JPU (Joint Pyramid Upsampling) for multi-scale feature fusion, and transformer blocks for contextual understanding. Leveraging deep-learning techniques, our proposed model outperforms existing approaches in retinal vascular segmentation, like achieving a Mean IOU of 0.8047, Recall of 0.7254, Precision of 0.8492, F1 Score of 0.7824, and Specificity of 0.9892 for CHASEDB1 dataset. Extensive evaluations on benchmark datasets demonstrate its efficacy, highlighting its potential for automated diabetic retinopathy screening.

Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine-learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using HiP-CT as part of the Human Organ Atlas Project. HiP-CT pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering a resolution of around 20 μm/voxel and enabling highly detailed localised zooms up to 1–2 μm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice similarity coefficient (DSC) scores of 0.9523, 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline DSC ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydrostatic pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Our study establishes the benchmark across various evaluation metrics, for vascular segmentation of HiP-CT imaging data, an imaging technology that has the potential to substantively shift our understanding of human vascular networks.

Effect of myogenic tone on agonist-mediated vasoconstriction in isolated arteries: A computational study

Background and objectiveVasoconstriction of the resistance artery is mainly determined by an integrated action of multiple local stimuli acting on the vascular smooth muscle cells, which include neuronal delivery of α-adrenoceptor agonists and intraluminal pressure. The contractile activity of the arterial wall has been extensively studied ex vivo using isolated arterial preparations and myography techniques. However, agonist-mediated vasoconstriction response is often confounded by local effects of other stimuli (e.g., pressure) and, it remained unclear whether the pressure-induced myogenic response has any implication on the efficacy of agonist-mediated vasoconstriction during blood flow regulation in tissues. A quantitative understanding of the influence of each stimulus is necessary to understand the interaction between multiple regulatory mechanisms, which is required to ensure timely oxygen delivery to meet tissue needs. MethodsWe developed a simple empirical model of isolated vessel vasoreactivity that includes passive vessel wall mechanics and a lumped representation of active smooth muscle activation as a function of agonist concentration and pressure. Pressure myograph data in dog renal arterioles and rat femoral arterioles, isovolumic myograph data in rat femoral arteries, and vasoactive data in rat skeletal muscle arterioles were analyzed using the model. The effect of physiological pressure changes on the sensitivities of vascular segments to adrenergic agonists phenylephrine and norepinephrine was evaluated. ResultsModel-based analysis of isolated vasoreactivity data, obtained due to changes in pressure and vasoconstricting agonists revealed that the strength of myogenic response of a resistance vessel has a strong influence on the sensitivity and dynamics of agonist response. An increase in intraluminal pressure was found to reduce the magnitude of agonist-mediated tone by lowering the sensitivity of the vessel segment to agonist. The passive mechanical properties of arterial wall considearably influence the agonist-mediated contraction in isolated arteries. These results demonstrate how passive vessel wall mechanics may dominate the vasoactive responses of the common myogenic and adrenergic pathways of smooth muscle contraction in blood flow regulation, supporting a long standing notion that there exists segment-specific vasoregulation in microvascular networks of various tissues. ConclusionThe present model provides a simple and powerful tool for quantifying ex vivo vasoreactivity of asolated arteries to qualitatively study the interaction between myogenic and α-adrenergic control of vascular tone in isolated vessels. Analysis of pressure myography data and isovolumic myography data in different sizes of vessels and tissues, in response to norepinephrine and phenylephrine revealed the importance of passive vessel mechanics in arteriolar vasomotion and setting up of basal vasomotor tone at single vessel-level. The present study will be useful to quantify the extent to which myogenic tone may influence agonist-mediated vasoconstriction and agonist effect on pressure-mediated myogenic response in microvascular networks during blood flow regulation in tissues.

Feasibility of Ultra-low Radiation and Contrast Medium Dosage in Aortic CTA Using Deep Learning Reconstruction at 60 kVp: An Image Quality Assessment

ObjectiveTo assess the viability of using ultra-low radiation and contrast medium (CM) dosage in aortic computed tomography angiography (CTA) through the application of low tube voltage (60kVp) and a novel deep learning image reconstruction algorithm (ClearInfinity, DLIR-CI). MethodsIodine attenuation curves obtained from a phantom study informed the administration of CM protocols. Non-obese participants undergoing aortic CTA were prospectively allocated into two groups and then obtained three reconstruction group. The conventional group (100kVp-CVgroup) underwent imaging at 100kVp and received 210 mg iodine/kg in combination with a hybrid iterative reconstruction algorithm (ClearView, HIR-CV). The experimental group was imaged at 60kVp with 105 mg iodine/kg, while images were reconstructed with HIR-CV (60 kVp-CV group) and with DLIR-CI (60 kVp-CI group). Student's t-test was used to compare differences in CM protocol and radiation dose. One-way ANOVA compared CT attenuation, image noise, SNR, and CNR among the three reconstruction groups, while the Kruskal-Wallis H test assessed subjective image quality scores. Post hoc analysis was performed with Bonferroni correction for multiple comparisons, and consistency analysis conducted in subjective image quality assessment was measured using Cohen's kappa. ResultsThe radiation dose (1.12±0.23 mSv vs. 2.03±0.82 mSv) and CM dosage (19.04±3.03 mL vs. 38.11±6.47 mL) provided the reduction of 45% and 50% in the experimental group compared to the conventional group. The CT attenuation, SNR and CNR of 60kVp-CI were superior to or equal to those of 100kVp-CV. Compared to the 60 kVp-CV group, images in 60kVp-CI showed higher SNR and CNR (all P < 0.001). There was no difference between the 60kVp-CI and 100kVp-CVgroup in terms of the subjective image quality of the aorta in various locations (all P > 0.05), with 60kVp-CI images were deemed diagnostically sufficient across all vascular segments. ConclusionsFor non-obese patients, the combined use of 60kVp and DLIR-CI algorithm can preserving image quality while enabling radiation dose and contrast medium savings for aortic CTA compared with 100kVp using HIR-CV.

Isosmotic Striction of Rat Aorta Smooth Muscle Cells During Activation of Purinergic Receptors: Role of Chlorine Transport

We studied the effect of the purinergic signaling system and Cl-transporters on vascular smooth muscle cells (SMC) isosmotic striction that occurs when osmotic pressure is normalized after prolonged incubation in a hypoosmotic medium. The study was performed with the method of myography on endothelium-denuded ring segments of the male Wistar rats aorta. Isosmotic striction was induced by placing the vascular segments in normosmotic Krebs solution containing 120 mM NaCl after a 40-minute incubation in a hyposmotic Krebs solution containing 40 mM NaCl. Purinergic receptors were activated by adenosine 5'-triphosphate (ATP, 500 μM) as nonselective P2X and P2Y receptor agonist, and uridine 5'-triphosphate (UTP, 500 μM) as selective P2Y receptor agonist. ATP and UTP eliminated the transient nature of the aorta SMC isosmotic striction without affecting its amplitude. Pretreatment of vascular segments with ATP and UTP during incubation in a hyposmotic solution completely suppressed the development of isosmotic striction in the presence of ATP or UTP, but did not affect isosmotic striction without activators of purinergic receptors. The inhibitor of Na+, K+, 2Cl--cotransport (NKCC) bumetanide (100 μM) abolished isosmotic striction in the presence of ATP, but not UTP, but restored its transient character. A non-selective blocker of Cl– channels and Cl–, HCO3– exchanger DIDS (100 μM) suppressed the development of isosmotic striction both in the presence of ATP and UTP. The potassium channel blocker tetraethylammonium (10 mM) potentiates the constrictor action of UTP on isosmotic striction. We suppose purinergic receptors eliminate the transient isosmotic striction by activating Cl– currents through activation of P2Y receptors. The mechanism of interaction between the purinergic signaling system and Cl– transport during changes in cell volume requires further study.

Expansion and identification of primary rat aortic vascular stem cells in vitro

To culture and expand primary rat aortic vascular stem cells in vitro and evaluate their characteristics as mesenchymal stem cells. The thoracic and abdominal aortas isolated from 2- to 3-week-old Sprague-Dawley rats were cut into vascular segments 2.0 mm in length and cultured in culture flasks till adhesion and solidification of the outer membranes. The primary cells were further cultured to 80%-90% confluence before passaging. The morphology and growth characteristics of the cells were observed under a microscope, and the expressions of surface marker CD molecules on the cells was analyzed using flow cytometry. Adipogenic and osteogenic differentiation assays were performed to assess the capacity of the cells for multilineage differentiation. After 3 days of culture, a small number of spindle, star-shaped or polygonal cells migrated out from the peripheral of the vascular segments. At 5-6 days, island-like cell clusters occurred and the cells began to proliferate rapidly. The cell clusters expanded radially and showed signs of cell cloning. At 7-8 days, the cells fused into sheets and displayed a vortex-like distribution. The cells in the third passage presented with a uniform morphology, showing a typical fibroblast-like arrangement. Flow cytometry showed that the cells expressed predominantly CD44 (80.3%), CD73 (62.2%) and CD90 (46.8%) with low expressions of CD34 (1.1%), CD45 (0.2%) and CD11b/c (0.2%). Adipogenic and osteogenic differentiation experiments demonstrated that the cells were capable of lipogenic and osteogenic differentiation in vitro. Rat aortic vascular stem cells with mesenchymal stem cell characteristics can be successfully isolated and cultured by adherent culture of the segmented outer membrane.

MedSyn: Text-Guided Anatomy-Aware Synthesis of High-Fidelity 3-D CT Images.

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. Algorithmic comparative assessments and blind evaluations conducted by 10 board-certified radiologists indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines and airways. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks.

Non-Invasive Assessment of Coronary Microvascular Dysfunction Using Vascular Deformation-Based Flow Estimation.

Non-invasive computation of the index of microcirculatory resistance from coronary computed tomography angiography (CTA), referred to as IMR[Formula: see text], is a promising approach for quantitative assessment of coronary microvascular dysfunction (CMD). However, the computation of IMR[Formula: see text] remains an important unresolved problem due to its high requirement for the accuracy of coronary blood flow. Existing CTA-based methods for estimating coronary blood flow rely on physiological assumption models to indirectly identify, which leads to inadequate personalization of total and vessel-specific flow. To overcome this challenge, we propose a vascular deformation-based flow estimation (VDFE) model to directly estimate coronary blood flow for reliable IMR[Formula: see text] computation. Specifically, we extract the vascular deformation of each vascular segment from multi-phase CTA. The concept of inverse problem solving is applied to implicitly derive coronary blood flow based on the physical constraint relationship between blood flow and vascular deformation. The vascular deformation constraints imposed on each segment within the vascular structure ensure sufficient individualization of coronary blood flow. Experimental studies on 106 vessels collected from 89 subjects demonstrate the validity of our VDFE, achieving an IMR[Formula: see text] accuracy of 82.08 %. The coronary blood flow estimated by VDFE has better reliability than the other four existing methods. Our proposed VDFE is an effective approach to non-invasively compute IMR[Formula: see text] with excellent diagnostic performance. The VDFE has the potential to serve as a safe, effective, and cost-effective clinical tool for guiding CMD clinical treatment and assessing prognosis.

A multi-task learning model for clinically interpretable sesamoiditis grading

Sesamoiditis is a common equine disease with varying severity, leading to increased injury risks and performance degradation in horses. Accurate grading of sesamoiditis is crucial for effective treatment. Although deep learning-based approaches for grading sesamoiditis show promise, they remain underexplored and often lack clinical interpretability. To address this issue, we propose a novel, clinically interpretable multi-task learning model that integrates clinical knowledge with machine learning. The proposed model employs a dual-branch decoder to simultaneously perform sesamoiditis grading and vascular channel segmentation. Feature fusion is utilized to transfer knowledge between these tasks, enabling the identification of subtle radiographic variations. Additionally, our model generates a diagnostic report that, along with the vascular channel mask, serves as an explanation of the model’s grading decisions, thereby increasing the transparency of the decision-making process. We validate our model on two datasets, demonstrating its superior performance compared to state-of-the-art models in terms of accuracy and generalization. This study provides a foundational framework for the interpretable grading of similar diseases.

Deep learning technology in vascular image segmentation and disease diagnosis

AbstractBlood vessel segmentation is a crucial aspect of medical image processing, aiding medical professionals in more accurate disease analysis and diagnosis. Manual blood vessel segmentation methods are time‐consuming and cumbersome, making the development of automatic segmentation methods essential. The rapid advancements in deep learning technology have introduced new tools and methods for vascular image segmentation. In this review, we provide a comprehensive overview of deep learning‐based blood vessel segmentation methods across various fields, including retinal vessel segmentation, cerebrovascular segmentation, and pulmonary vessel segmentation. Several prevalent diseases, such as retinal vascular diseases, cerebrovascular diseases, pulmonary vascular diseases, and tumors, have posed significant health challenges globally. This review also discusses the application of deep learning technology in disease diagnosis within these contexts. Finally, considering the current research landscape, we discuss existing challenges and potential future developments in blood vessel segmentation. We aim to assist researchers in gaining a comprehensive understanding and designing effective blood vessel segmentation models, ultimately offering opportunities for early disease diagnosis and treatment.

Age-peculiarities of L-arginine, inosine and meldonium effects on the adult and old rat vascular wall reactivity

Abstract. Cardiovascular diseases are most common in older people. The functional state of the vascular endothelium is important in the pathogenesis of cardiovascular diseases. Numerous physiologically active substances with different mechanisms of action can affect endothelial function L-arginine, inosine and meldonium caught our attention. Combination therapy is a promising direction in treating circulatory system diseases. This work aimed to establish the age-related characteristics of L-arginine, inosine and meldonium and their combination (Melarginine) on the vascular wall reactivity in adult and old rats to substantiate the occurrence of these cases in cardiology and geriatrics. Experiments were carried out on isolated norepinephrine (10-7 mol/l) segments of the thoracic aorta of adult (8-10 months) and old (21-23 months) outbred male rats. It was shown that the vasodilatory effect of L-arginine, inosine and meldonium and their combination (Melarginine) was insignificant. At the same time, the isolated vascular segment relaxation of old rats under the studied substances was significantly greater compared to adult animals. It should be noted that the vasodilatory effect of acetylcholine in old rats was significantly less than in adults. This indicates the practical endothelium preservation in adult animals and its damage in old ones. The greater sensitivity of the vascular wall of old rats to the action of L-arginine, inosine and meldonium and their combination Melarginine indicates their action is directed at restoring endothelial function in old animals. This suggests the feasibility of using Melarginine in geriatric practice for cardiovascular disease treatment in the future. _________________________________________________________________________________________ Keywords: vessel reactivity; ageing; L-arginine; inosine; meldonium; Melarginine

Analysis and comparison of retinal vascular parameters under different glucose metabolic status based on deep learning.

To develop a deep learning-based model for automatic retinal vascular segmentation, analyzing and comparing parameters under diverse glucose metabolic status (normal, prediabetes, diabetes) and to assess the potential of artificial intelligence (AI) in image segmentation and retinal vascular parameters for predicting prediabetes and diabetes. Retinal fundus photos from 200 normal individuals, 200 prediabetic patients, and 200 diabetic patients (600 eyes in total) were used. The U-Net network served as the foundational architecture for retinal artery-vein segmentation. An automatic segmentation and evaluation system for retinal vascular parameters was trained, encompassing 26 parameters. Significant differences were found in retinal vascular parameters across normal, prediabetes, and diabetes groups, including artery diameter (P=0.008), fractal dimension (P=0.000), vein curvature (P=0.003), C-zone artery branching vessel count (P=0.049), C-zone vein branching vessel count (P=0.041), artery branching angle (P=0.005), vein branching angle (P=0.001), artery angle asymmetry degree (P=0.003), vessel length density (P=0.000), and vessel area density (P=0.000), totaling 10 parameters. The deep learning-based model facilitates retinal vascular parameter identification and quantification, revealing significant differences. These parameters exhibit potential as biomarkers for prediabetes and diabetes.

Edge morphology attention mechanism and optimal geometric matching connection model for vascular segmentation

Over the past decades, medical image segmentation methods have been intensively developed, but there are still a number of unsolved challenging problems in vascular image segmentation, including broken vessels, insufficient vessel branches, and missing small vessels. In order to optimize the topology and accuracy of segmented vessels, we propose a novel Edge Morphology Attention Network (EMA-Net) for the segmentation of vessel-like structures, and an Optimal Geometric Matching Connection (OGMC) model to connect the broken vessel fragments. The EMA-Net has an edge attention module that is able to improve segmented performances of edges and small branches by morphology operation extracting boundary voxels on multi-scales. The OGMC model uses the geometry concept of curve touching to filter out fragmented vessel endpoints, and then employs minimal surfaces to determine the optimal connection order between blood vessels. Finally, we adopt geodesics to repair missing vessels under a Riemannian metric. Our method achieves superior or competitive results compared to state-of-the-art methods on four datasets of 3D vascular segmentation tasks, both effectively reducing vessel broken and increasing vessel branch richness, yielding blood vessels with more precise topological structures.

Endothelial Cells Increase Mesenchymal Stem Cell Differentiation in Scaffold-Free 3D Vascular Tissue.

In this study, we present a versatile, scaffold-free approach to create ring-shaped engineered vascular tissue segments using human mesenchymal stem cell-derived smooth muscle cells (hMSC-SMCs) and endothelial cells (ECs). We hypothesized that incorporation of ECs would increase hMSC-SMC differentiation without compromising tissue ring strength or fusion to form tissue tubes. Undifferentiated hMSCs and ECs were co-seeded into custom ring-shaped agarose wells using four different concentrations of ECs: 0%, 10%, 20%, and 30%. Co-seeded EC and hMSC rings were cultured in SMC differentiation medium for a total of 22 days. Tissue rings were then harvested for histology, Western blotting, wire myography, and uniaxial tensile testing to examine their structural and functional properties. Differentiated hMSC tissue rings comprising 20% and 30% ECs exhibited significantly greater SMC contractile protein expression, endothelin-1 (ET-1)-meditated contraction, and force at failure compared with the 0% EC rings. On average, the 0%, 10%, 20%, and 30% EC rings exhibited a contractile force of 0.745 ± 0.117, 0.830 ± 0.358, 1.31 ± 0.353, and 1.67 ± 0.351 mN (mean ± standard deviation [SD]) in response to ET-1, respectively. Additionally, the mean maximum force at failure for the 0%, 10%, 20%, and 30% EC rings was 88.5 ± 36. , 121 ± 59.1, 147 ± 43.1, and 206 ± 0.8 mN (mean ± SD), respectively. Based on these results, 30% EC rings were fused together to form tissue-engineered blood vessels (TEBVs) and compared with 0% EC TEBV controls. The addition of 30% ECs in TEBVs did not affect ring fusion but did result in significantly greater SMC protein expression (calponin and smoothelin). In summary, co-seeding hMSCs with ECs to form tissue rings resulted in greater contraction, strength, and hMSC-SMC differentiation compared with hMSCs alone and indicates a method to create a functional 3D human vascular cell coculture model.