Search Results (68)

Fluoroscopy (real-time X-ray) images are used for monitoring minimally invasive coronary angioplasty operations such as stent placement. During these operations, a thin wire called a guidewire is used to guide different tools, such as a stent or a balloon, in order to repair the vessels. However, fluoroscopy images are noisy, and the guidewire is very thin, practically invisible in many places, making its localization very difficult. Guidewire segmentation is the task of finding the guidewire pixels, while guidewire localization is the higher-level task aimed at finding a parameterized curve describing the guidewire points. This paper presents a method for guidewire localization that starts from a guidewire segmentation, from which it extracts a number of initial curves as pixel chains and uses a novel perceptual grouping method to merge these initial curves into a small number of curves. The paper also introduces a novel guidewire segmentation method that uses a residual network (ResNet) as a feature extractor and predicts a coarse segmentation that is refined only in promising locations to a fine segmentation. Experiments on two challenging datasets, one with 871 frames and one with 23,449 frames, show that the method obtains results competitive with existing segmentation methods such as Res-UNet and nnU-Net, while having no skip connections and a faster inference time. Full article

Retinal vessel segmentation is crucial for analyzing medical images, where symmetry in vascular structures plays a fundamental role in diagnostic accuracy. In recent years, the rapid advancements in deep learning have provided robust tools for predicting detailed images. However, within many scenarios of medical image analysis, the task of data annotation remains costly and challenging to acquire. By leveraging symmetry-aware semi-supervised learning frameworks, our approach requires only a small portion of annotated data to achieve remarkable segmentation outcomes, significantly diminishing the costs associated with data labeling. At present, most semi-supervised approaches rely on pseudo-label update strategies. Nonetheless, while these methods generate high-quality pseudo-label images, they inevitably contain minor prediction errors in a few pixels, which can accumulate during iterative training, ultimately impacting learner performance. To address these challenges, we propose an enhanced semi-supervised vessel semantic segmentation approach that employs a symmetry-preserving pixel-level filtering strategy. This method retains highly reliable pixels in pseudo labels while eliminating those with low reliability, ensuring spatial symmetry coherence without altering the intrinsic spatial information of the images. The filtering strategy integrates various techniques, including probability-based filtering, edge detection, image filtering, mathematical morphology methods, and adaptive thresholding strategies. Each technique plays a unique role in refining the pseudo labels. Extensive experimental results demonstrate the superiority of our proposed method, showing that each filtering strategy contributes to enhancing learner performance through symmetry-constrained optimization. Full article

Accurate cerebral vasculature segmentation using digital subtraction angiography (DSA) is critical for diagnosing and treating cerebrovascular diseases. However, conventional single-frame analysis methods often fail to capture fine vascular structures due to background noise, overlapping anatomy, and dynamic contrast flow. In this study, we propose a novel vessel-enhancing preprocessing technique using temporal differencing of DSA sequences to improve cerebrovascular segmentation accuracy. Our method emphasizes contrast flow dynamics while suppressing static background components by computing absolute differences between sequential DSA frames. The enhanced images were input into state-of-the-art deep learning models, U-Net++ and DeepLabv3+, for vascular segmentation. Quantitative evaluation of the publicly available DIAS dataset demonstrated significant segmentation improvements across multiple metrics, including the Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and Vascular Connectivity (VC). Particularly, DeepLabv3+ with the proposed preprocessing achieved a DSC of 0.83 ± 0.05 and VC of 44.65 ± 0.63, outperforming conventional methods. These results suggest that leveraging temporal information via input enhancement substantially improves small and complex vascular structure extraction. Our approach is computationally efficient, model-agnostic, and clinically applicable for DSA. Full article

Background: Vascular risk factors (VRFs) are known to influence cerebral small vessel disease (cSVD) burden and progression. However, their specific impact on the presence and distribution of each cSVD imaging marker (white matter hyperintensity [WMH], perivascular spaces [PVSs], lacunes, and cerebral microbleeds [CMBs]) and their spatial distribution remains unclear. Methods: We conducted a retrospective analysis of 93 patients with lacunar stroke with a standardized investigational magnetic resonance imaging protocol using a 3T scanner. WMH and PVSs were segmented semi-automatically, and lacunes and CMBs were manually segmented. We assessed the univariable associations of four common VRFs (hypertension, hyperlipidemia, diabetes, and smoking) with the load of each cSVD marker. Then, we assessed the independent associations of these VRFs in multivariable regression models adjusted for age and sex. Spatial lesion patterns were explored with regional volumetric comparisons using Pearson’s coefficient analysis, which was adjusted for multiple comparisons, and by visually examining heatmap lesion distributions. Results: Hypertension was the VRF that exhibited stronger associations with the cSVD markers in the univariable analysis. In the multivariable analysis, only lacunes (p = 0.009) and PVSs in the basal ganglia (p = 0.014) and white matter (p = 0.016) were still associated with hypertension. In the regional analysis, hypertension showed a higher WMH load in deep structures and white matter, particularly in the posterior periventricular regions. In patients with hyperlipidemia, WMH was preferentially found in hippocampal regions. Conclusions: Hypertension was confirmed to be the VRF with the most impact on cSVD load, especially for lacunes and PVSs, while the lesion topography was variable for each VRF. These findings shed light on the complexity of cSVD expression in relation to factors detrimental to vascular health. Full article

The accurate segmentation of blood vessels and centerline extraction are critical in vascular imaging applications, ranging from preoperative planning to hemodynamic modeling. This study introduces a novel one-stage method for simultaneous vessel segmentation and centerline extraction using a multitask neural network. We designed a hybrid architecture that integrates convolutional and graph layers, along with a task-specific loss function, to effectively capture the topological relationships between segmentation and centerline extraction, leveraging their complementary features. The proposed end-to-end framework directly predicts the centerline as a polyline with real-valued coordinates, thereby eliminating the need for post-processing steps commonly required by previous methods that infer centerlines either implicitly or without ensuring point connectivity. We evaluated our approach on a combined dataset of 142 computed tomography angiography images of the thoracic and abdominal regions from LIDC-IDRI and AMOS datasets. The results demonstrate that our method achieves superior centerline extraction performance (Surface Dice with threshold of 3 mm: 97.65%$\pm$2.07%) compared to state-of-the-art techniques, and attains the highest subvoxel resolution (Surface Dice with threshold of 1 mm: 72.52%$\pm$8.96%). In addition, we conducted a robustness analysis to evaluate the model stability under small rigid and deformable transformations of the input data, and benchmarked its robustness against the widely used VMTK toolkit. Full article

High-completeness ship trajectory data are critical for analyzing navigation behavior characteristics and enhancing effective maritime management. To address the common issues of prolonged AIS data loss for small coastal vessels in nearshore waters, an intelligent trajectory repairing model based on the artificial potential field-enhanced A* algorithm (APF-A*) has been proposed. Kernel density estimation was utilized to quantify the distribution characteristics of vessels, thereby constructing an attractive potential field based on historical trajectories and a repulsive potential field based on coastal terrain. Speed distribution characteristics were extracted from historical trajectory points in different regions; on the basis of this, the A* algorithm, integrated with attractive and repulsive fields, was proposed to repair missing trajectory segments. Based on the speed distribution characteristics, time intervals, and distance information, the temporal information of the vessel trajectories was effectively reconstructed. The present study fills the research gap in AIS data reconstruction for small coastal vessels in complex coastal waters. A case study has been conducted in Luoyuan Bay, Fujian Province, China, to further validate the proposed model. The results demonstrate that the trajectory repairing model based on the artificial potential field-enhanced A* algorithm outperformed other models. More specifically, the Hausdorff Distance and Dynamic Time Warping (DTW) metrics decreased by 81.67% and 91.56%, respectively. The present study shares useful insights into intelligent maritime management and further supports accident prevention in coastal waters. Full article

Accurate segmentation of brain vessels is critical for diagnosing cerebral stroke, yet existing AI-based methods struggle with challenges such as small vessel segmentation and class imbalance. To address this, our study proposes a novel 2D segmentation method based on the nnUNet framework, enhanced with MedSAM/MedSAM2 features, for arterial vessel segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) brain slices. The approach first constructs a baseline segmentation network using nnUNet, then incorporates MedSAM/MedSAM2’s feature extraction module to enhance feature representation. Additionally, focal loss is introduced to address class imbalance. Experimental results on the CAS2023 dataset demonstrate that the MedSAM2-enhanced model achieves a 0.72% relative improvement in Dice coefficient and reduces HD95 (mm) and ASD (mm) from 48.20 mm to 46.30 mm and from 5.33 mm to 4.97 mm, respectively, compared to the baseline nnUNet, showing significant enhancements in boundary localization and segmentation accuracy. This approach addresses the critical challenge of small vessel segmentation in TOF-MRA, with the potential to improve cerebrovascular disease diagnosis in clinical practice. Full article

A newly recognized fourth type of perivascular space has recently been described in the radiological literature. Despite its growing relevance, many radiologists are still unfamiliar with its imaging characteristics, often leading to misinterpretation as cystic neoplasms. Due to its potential for diagnostic confusion, further studies are necessary—particularly those incorporating high-quality imaging examples across various presentations—to facilitate accurate recognition and classification. Perivascular spaces (PVSs) of the brain are cystic, fluid-filled structures formed by the pia mater and located alongside cerebral blood vessels, particularly penetrating arterioles, venules, and capillaries. Under normal conditions, these spaces are small (typically <2 mm in diameter), but in rare instances, they may become markedly enlarged (>15 mm), exerting a mass effect on adjacent brain tissue. This newly identified fourth type of PVS is found in association with the M2 and M3 segments of the middle cerebral artery, typically within the anterior temporal lobe white matter. It may mimic low-grade cystic tumors on imaging due to its size and frequent presence of surrounding perifocal edema. We present two adult male patients with this rare PVS variant. The first patient, a 63-year-old, had a brain magnetic resonance imaging scan (MRI) that revealed a cystic lesion in the white matter of the right temporal lobe anterior pole, near the middle cerebral artery M2 segment, with perifocal vasogenic edema. The second patient, a 67-year-old, had a brain MRI that showed a cystic lesion in the white matter and subcortical region of the right temporal lobe anterior pole, with minimal surrounding gliosis or minimal edema. The cystic lesions in both patients remained unchanged over time on follow-up MRI. These cases illustrate the radiological complexity of this under-recognized entity and emphasize the importance of differential diagnosis to avoid unnecessary intervention. Full article

Coronary artery disease (CAD) is a highly prevalent cardiovascular disease and one of the leading causes of death worldwide. The accurate segmentation of coronary arteries from CT angiography (CTA) images is essential for the diagnosis and treatment of coronary artery disease. However, due to small vessel diameters, large morphological variations, low contrast, and motion artifacts, conventional segmentation methods, including classical image processing (such as region growing and level sets) and early deep learning models with limited receptive fields, are unsatisfactory. We propose SADiff, a hybrid framework that integrates a dilated attention network (DAN) for ROI extraction, a diffusion-based subnet for noise suppression in low-contrast regions, and a striped attention network (SAN) to refine tubular structures affected by morphological variations. Experiments on the public ImageCAS dataset show that it has a Dice score of 83.48% and a Hausdorff distance of 19.43 mm, which is 6.57% higher than U-Net3D in terms of Dice. The cross-dataset validation on the private ImageLaPP dataset verifies its generalizability with a Dice score of 79.42%. This comprehensive evaluation demonstrates that SADiff provides a more efficient and versatile method for coronary segmentation and shows great potential for improving the diagnosis and treatment of CAD. Full article

The spatial arrangement, size distribution, and shape of internal pores in several archaeological ceramic vessels from the Akterek burial site at Zhambyl District of Almaty Region, Republic of Kazakhstan were studied using neutron tomography. The internal pores were segmented from the obtained neutron data and the porosity value for the ancient ceramic samples was calculated. Analysis of the structural tomography data showed that the ceramic materials contained a large number of relatively small pores, with an average diameter less than 1.5 mm, while some ceramic objects had larger pores or cavities exceeding 2 mm in diameter. In addition, there are differences in the morphological parameters of large and small pores. It was suggested that these large pores formed as a result of temperature changes during the firing of the pottery ceramics. The relative shifting of Raman peaks in the carbon group in amorphous carbon, as an indicator of the firing temperature of ceramic materials, confirms this assumption. Full article

This paper analyzes the potential of retrofitting in “greening” of European inland vessels and coastal ships, which are normally not the focus of major international environmental policies aimed at waterborne transport. Therefore, greening of the examined fleets would result, for the most part, in additional emission reductions to the environmental targets put forth by the International Maritime Organization. By scoping past and ongoing pilot projects, the most prominent retrofit trends in the greening of inland and coastal ships are identified. Assuming a scenario in which the observed trends are scaled up to the fleet level, the possible emission abatement is estimated (both on the tank-to-wake and well-to-wake bases), as well as the capital and operational costs associated with the retrofit. Therefore, the paper shows what can be achieved in terms of greening if the current trends are followed. The results show that the term “greening” may take a significantly different meaning contingent on the approaches, perspectives, and targets considered. The total costs of a retrofit of a single vessel may be excessively high; however, the costs may significantly vary depending on the vessel power requirements, operational profile, and technology applied. While some trends are worth following (electrification of ferries and small inland passenger ships), others may be too cost-intensive and not satisfactorily efficient in terms of emissions reduction (retrofit of offshore supply vessels with dual-fuel methanol engines). Nevertheless, the assessment of different retrofit technologies strongly depends on the adopted criteria, including but not limited to the total cost of the retrofit of the entire fleet segment, cost of the retrofit of a single vessel, emission abatement achieved by the retrofit of a fleet segment, average emission abatement per retrofitted vessel, and cost of abatement of one ton of greenhouse gases, etc. Full article

The primary challenges in the tissue engineering of small-diameter artificial blood vessels include inadequate mechanical properties and insufficient anticoagulation capabilities. To address these challenges, urea-pyrimidone (Upy)-based polyurethane elastomers (PIIU-B) were synthesized by incorporating quadruple hydrogen bonding within the polymer backbone. The synthesis process employed poly(L-lactide-ε-caprolactone) (PLCL) as the soft segment, while di-(isophorone diisocyanate)-Ureido pyrimidinone (IUI) and isophorone diisocyanate (IPDI) were utilized as the hard segment. The resulting PIIU-B small-diameter artificial blood vessel with a diameter of 4 mm was fabricated using the electrospinning technique, achieving an optimized IUI/IPDI composition ratio of 1:1. Enhanced by multiple hydrogen bonds, the vessels exhibited a robust elastic modulus of 12.45 MPa, an extracellular matrix (ECM)-mimetic nanofiber morphology, and a high porosity of 41.31%. Subsequently, the PIIU-B vessel underwent dual-functionalization with low-molecular-weight heparin and gelatin via ultraviolet (UV) crosslinking (designated as PIIU-B@LHep/Gel), which conferred superior biocompatibility and exceptional anticoagulation properties. The study revealed improved anti-platelet adhesion characteristics as well as a prolonged activated partial thromboplastin time (APTT) of 157.2 s and thrombin time (TT) of 64.2 s in vitro. Following a seven-day subcutaneous implantation, the PIIU-B@LHep/Gel vessel exhibited excellent biocompatibility, evidenced by complete integration with the surrounding peri-implant tissue, significant cell infiltration, and collagen formation in vivo. Consequently, polyurethane-based artificial blood vessels, reinforced by multiple hydrogen bonds and dual-functionalized with heparin and gelatin, present as promising candidates for vascular tissue engineering. Full article

Background: Unilateral basal ganglia calcinosis (BGC) is a rare radiological finding that can be diagnosed on computed tomography (CT) and magnetic resonance imaging (MRI) but often presents challenges for clinicians and radiologists in determining its underlying cause. So far, only a few potential causes that could explain unilateral BGC have been described in the literature. Case Report: A 54-year-old Caucasian male was admitted to a tertiary university hospital due to the sudden onset of speech impairment and right-sided weakness. The patient had no significant medical history prior to this event. Non-enhanced computed tomography (NECT) of the brain revealed no evidence of acute ischemia; CT angiography (CTA) showed acute left middle cerebral artery (MCA) M2 segment occlusion. CT perfusion (CTP) maps revealed an extensive penumbra-like lesion, which is potentially reversible upon achieving successful recanalization. However, a primary neoplastic tumor with calcifications in the basal ganglia was initially interpreted as the potential cause; therefore, acute stroke treatment with intravenous thrombolysis was contraindicated. A follow-up CT examination at 24 h revealed an ischemic lesion localized to the left insula, predominantly involving the left parietal lobe and the superior gyrus of the left temporal lobe. Subsequent gadolinium-enhanced brain MRI revealed small blood vessels draining into the subependymal periventricular veins on the left basal ganglia. Digital subtraction angiography was conducted, confirming the diagnosis of venous angioma. Conclusions: Unilateral BGC caused by venous angioma is a rare entity with unclear pathophysiological mechanisms and heterogeneous clinical presentation. It may mimic conditions such as intracerebral hemorrhage or hemorrhagic brain tumors, complicating acute stroke management, as demonstrated in this case. Surrounding tissue calcification may provide a valuable radiological clue in diagnosing venous angiomas DVAs and vascular malformations. Full article

Background: Liver cancer has a high mortality rate worldwide, and clinicians segment liver vessels in CT images before surgical procedures. However, liver vessels have a complex structure, and the segmentation process is conducted manually, so it is time-consuming and labor-intensive. Consequently, it would be extremely useful to develop a deep learning-based automatic liver vessel segmentation method. Method: As a segmentation method, UNet is widely used as a baseline, and a multi-scale block or attention module has been introduced to extract context information. In recent machine learning efforts, not only has the global context extraction been improved by introducing Transformer, but a method to reinforce the edge area has been proposed. However, the data preprocessing step still commonly uses general augmentation methods, such as flip, rotation, and mirroring, so it does not perform robustly on images of varying brightness or contrast levels. We propose a method of applying image augmentation with different HU windowing values. In addition, to minimize the false negative area, we propose TransRAUNet, which introduces a reverse attention module (RAM) that can focus edge information to the baseline TransUNet. The proposed architecture solves context loss for small vessels by applying edge module (RAM) in the upsampling phase. It can also generate semantic feature maps that allows it to learn edge, global context, and detail location by combining high-level edge and low-level context features. Results: In the 3Dricadb dataset, the proposed model achieved a DSC of 0.948 and a sensitivity of 0.944 in liver vessel segmentation. This study demonstrated that the proposed augmentation method is effective and robust by comparisons with the model without augmentation and with the general augmentation method. Additionally, an ablation study showed that RAM has improved segmentation performance compared to TransUNet. Compared to prevailing state-of-the-art methods, the proposed model showed the best performance for liver vessel segmentation. Conclusions: TransRAUnet is expected to serve as a navigation aid for liver resection surgery through accurate liver vessel and tumor segmentation. Full article

Numerous efforts have been invested in previous algorithms to expose and enhance blood vessel (BV) visibility derived from clinical coronary angiography (CAG) procedures, such as noise reduction, segmentation, and background subtraction. Yet, the visibility of the BVs and their luminal content, particularly the small ones, is still limited. We propose a novel visibility enhancement algorithm, whose main body is inspired by a line completion mechanism of the visual system, i.e., lateral interactions. It facilitates the enhancement of the BVs along with simultaneous noise reduction. In addition, we developed a specific algorithm component that allows better visibility of small BVs and the various CAG tools utilized during the procedure. It is accomplished by enhancing the BVs’ fine resolutions, located in the coarse resolutions at the BV zone. The visibility of the most significant clinical features during the CAG procedure was evaluated and qualitatively compared by the consensus of two cardiologists (MM and JE) to the algorithm’s results. These included the visibility of the whole frame, the coronary BVs as well as the small ones, the main obstructive lesions within the BVs, and the various angiography interventional tools utilized during the procedure. The algorithm succeeded in producing better visibility of all these features, even under low-contrast or low-radiation conditions. Despite its major advantages, the algorithm also caused the appearance of disturbing vertebral and bony artifacts, which could somewhat lower diagnostic accuracy. Yet, viewing the processed images from multiple angles and not just from a single one and evaluating the cine mode usually overcomes this drawback. Thus, our novel algorithm potentially leads to a better clinical diagnosis, improved procedural capabilities, and a successful outcome. Full article