Vascular Centerline Research Articles

Distinctive Phase Interdependency Model for Retinal Vasculature Delineation in OCT-Angiography Images.

Automatic detection of retinal vasculature in optical coherence tomography angiography (OCTA) images faces several challenges such as the closely located capillaries, vessel discontinuity and high noise level. This paper introduces a new distinctive phase interdependency model to address these problems for delineating centerline patterns of the vascular network. We capture the inherent property of vascular centerlines by obtaining the inter-scale dependency information that exists between neighboring symmetrical wavelets in complex Poisson domain. In particular, the proposed phase interdependency model identifies vascular centerlines as the distinctive features that have high magnitudes over adjacent symmetrical coefficients whereas the coefficients caused by background noises are decayed rapidly along adjacent wavelet scales. The potential relationships between the neighboring Poisson coefficients are established based on the coherency of distinctive symmetrical wavelets. The proposed phase model is assessed on the OCTA-500 database (300 OCTA images + 200 OCT images), ROSE-1-SVC dataset (9 OCTA images), ROSE-1 (SVC+ DVC) dataset (9 OCTA images), and ROSE-2 dataset (22 OCTA images). The experiments on the clinically relevant OCTA images validate the effectiveness of the proposed method in achieving high-quality results. Our method produces average FScore of 0.822, 0.782, and 0.779 on ROSE-1-SVC, ROSE-1 (SVC+ DVC), and ROSE-2 datasets, respectively, and the FScore of 0.910 and 0.862 on OCTA_6mm and OCT_3mm datasets (OCTA-500 database), respectively, demonstrating its superior performance over the state-of-the-art benchmark methods.

X-ray coronary centerline extraction based on C-UNet and a multifactor reconnection algorithm

Background and objective: Accurate extraction of the coronary artery centerline is crucial in the processes of coronary artery reconstruction, coronary artery stenosis or lesion detection, and surgical navigation. Furthermore, in clinical medicine, the complex background of angiography, low signal-to-noise ratio, and complex vascular structure make coronary artery centerline extraction challenging. In this study, a direct centerline extraction method is proposed that automatically and accurately extracts vascular centerlines from X-ray coronary angiography images based on deep learning and conventional methods.Methods: In this study, a coronary artery centerline extraction method is proposed that comprises two parts: the preliminary centerline extraction network based on U-Net with a residual network, called C-UNet, and the multifactor centerline reconnection algorithm based on the geometric characteristics of blood vessels.Results: The qualitative and quantitative results demonstrate the effectiveness of the presented method. In this study, three widely used evaluation indices were adopted to evaluate the performance of the method: precision, recall, and F1_Score. The experimental results show that this method can accurately extract coronary artery centerlines.Conclusions: The proposed centerline extraction method accurately extracts centerlines from X-ray coronary angiography images and improves both the accuracy and continuity of centerline extraction.

Development of an approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms.

Purpose: In stable coronary artery disease (CAD), reduction in mortality and/or myocardial infarction with revascularization over medical therapy has not been reliably achieved. Coronary arteries are usually extracted to perform stenosis detection. As such, developing accurate segmentation of vascular structures and quantification of coronary arterial stenosis in invasive coronary angiograms (ICA) is necessary. Approach: A multi-input and multiscale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. The proposed model generated a refined prediction map with the following two training stages: (i)stage I coarsely segmented the major coronary arteries from preprocessed single-channel ICAs and generated the probability map of arteries; and (ii)during the stage II, a three-channel image consisting of the original preprocessed image, a generated probability map, and an edge-enhanced image generated from the preprocessed image was fed to the proposed MIMS U-Net to produce the final segmentation result. After segmentation, an arterial stenosis detection algorithm was developed to extract vascular centerlines and calculate arterial diameters to evaluate stenotic level. Results: Experimental results demonstrated that the proposed method achieved an average Dice similarity coefficient of 0.8329, an average sensitivity of 0.8281, and an average specificity of 0.9979 in our dataset with 294 ICAs obtained from 73 patients. Moreover, our stenosis detection algorithm achieved a true positive rate of 0.6668 and a positive predictive value of 0.7043. Conclusions: Our proposed approach has great promise for clinical use and could help physicians improve diagnosis and therapeutic decisions for CAD.

Algorithm of Pulmonary Vascular Segment and Centerline Extraction.

Because pulmonary vascular lesions are harmful to the human body and difficult to detect, computer-assisted diagnosis of pulmonary blood vessels has become the focus and difficulty of the current research. An algorithm of pulmonary vascular segment and centerline extraction which is consistent with the physician's diagnosis process is proposed for the first time. We construct the projection of maximum density, restore the vascular space information, and correct random walk algorithm to satisfy automatic and accurate segmentation of blood vessels. Construct a local 3D model to restrain Hessian matrix when extracting centerline. In order to assist the physician to make a correct diagnosis and verify the effectiveness of the algorithm, we proposed a visual expansion model. According to the 420 high-resolution CT data of lung blood vessels labeled by physicians, the accuracy of segmentation algorithm AOM reached 93%, and the processing speed was 0.05 s/frame, which achieved the clinical application standards.

Automatic extraction and stenosis evaluation of coronary arteries in invasive coronary angiograms

BackgroundCoronary artery disease (CAD) is the leading cause of death in the United States (US) and a major contributor to healthcare cost. Accurate segmentation of coronary arteries and detection of stenosis from invasive coronary angiography (ICA) are crucial in clinical decision making. PurposeWe aim to develop an automatic method to extract coronary arteries by deep learning and detect arterial stenosis from ICAs. MethodsIn this study, a deep learning model which integrates a feature pyramid with a U-Net++ model was developed to automatically segment coronary arteries in ICAs. A compound loss function which contains Dice loss, dilated Dice loss, and L2 regularization was utilized to train the proposed segmentation model. Following the segmentation, an algorithm which extracts vascular centerlines, calculates the diameters, and measures the stenotic levels, was developed to detect arterial stenosis. Results and conclusionsIn the dataset consisting of 314 ICAs obtained from 99 patients, the segmentation model achieved an average Dice score of 0.8899, a sensitivity of 0.8595, and a specificity of 0.9960. In addition, the stenosis detection algorithm achieved a true positive rate of 0.6840 and a positive predictive value of 0.6998 on all types of stenosis, which has great promise to advance to clinical uses and could provide auxiliary suggestions for CAD diagnosis and treatment.

Path planning for endovascular catheterization under curvature constraints via two-phase searching approach

PurposePlanning a safe path for flexible catheters is one of the major challenges of endovascular catheterization. State-of-the-art methods rarely consider the catheter curvature constraint and reduced computational time of path planning which guarantees the possibility to re-plan the path during the actual operation. MethodsIn this manuscript, we propose a fast two-phase path planning approach under the robot curvature constraint. Firstly, the vascular structure is extracted and represented by vascular centerlines and corresponding vascular radii. Then, the path is searched along the vascular centerline using breadth first search (BFS) strategy and locally optimized via the genetic algorithm (GA) to satisfy the robot curvature constraint. This approach (BFS-GA) is able to respect the robot curvature constraint while keeping it close to the centerlines as much as possible. We can also reduce the optimization search space and perform parallel optimization to shorten the computational time. ResultsWe demonstrate the method’s high efficiency in two-dimensional and three-dimensional space scenarios. The results showed the planner’s ability to satisfy the robot curvature constraint while keeping low computational time cost compared with sampling-based methods. Path replanning in femoral arteries can reach an updating frequency at 6.4pm 2.3Hz. ConclusionThe presented work is suited for surgical procedures demanding satisfying curvature constraints while optimizing specified criteria. It is also applicable for curvature constrained robots in narrow passages.

Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks.

Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of vascular structures. Recent graphing methods can provide smooth vessels trajectories and well-connected vascular topology. However, they require water-tight surface meshes as inputs. Furthermore, adding vessels radii information on their graph compartments restricts their alignment along vascular centerlines. Here, we propose a novel graphing scheme that works with relaxed input requirements and intrinsically captures vessel radii information. The proposed approach is based on deforming geometric graphs constructed within vascular boundaries. Under a laplacian optimization framework, we assign affinity weights on the initial geometry that drives its iterative contraction toward vessels centerlines. We present a mechanism to decimate graph structure at each run and a convergence criterion to stop the process. A refinement technique is then introduced to obtain final vascular models. Our implementation is available on https://github.com/Damseh/VascularGraph. We benchmarked our results with that obtained using other efficient and state-of-the-art graphing schemes, validating on both synthetic and real angiograms acquired with different imaging modalities. The experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. Furthermore, it surpasses other techniques in providing representative models that capture all anatomical aspects of vascular structures.

Artery-venous classification in fluorescein angiograms based on region growing with sequential and structural features

Background and objectivesFluorescein angiography (FA) is widely used in ophthalmology for examining retinal hemodynamics and vascular morphology. Artery-venous classification is an important step in FA image processing for measurement of feature parameters, such as arterio-venous passage time (AVP) and arterio-venous width ratio (AVR) that are proven useful in clinical assessment of circulation disturbance and vessel abnormalities. However, manual artery-venous classification needs expertise and is rather time consuming, and little effort has been devoted to develop automatic classification methods. In order to solve this problem, we propose a novel artery-venous classification method using region growing strategy with sequential and structural features (RGSS). MethodsThe main procedures of our proposed RGSS method include: (i) registration of FA image sequence by mutual-information method; (ii) extraction of sequential features of the dye perfusion process from the registrated FA images; (iii) extraction of vessel structural features from vascular centerline map; (iv) based on the obtained features, seeds of arteries and veins within initial growing region (here optic disk) are generated and then propagated in the entire vessel network using region growing strategy. The RGSS method was tested on our own dataset and public Duke dataset, and its performance was evaluated quantitatively. ResultsTests show that RGSS method is able to classify arteries and veins from the complicated vessel network in FA images, with high classification accuracy of 0.91 ± 0.04 on Duke dataset and 0.92 ± 0.03 on our dataset. The employed sequential and structural features are demonstrated to be effective in classifying thin arteries and veins at vessel crossings. ConclusionsAutomatic artery-venous classification can be accomplished using our proposed RGSS method with high accuracy. The RGSS method not only emancipates ophthalmologists from hard work of manual marking of arteries and veins, but also helps in measuring important parameters (such as AVP and AVR) for clinical assessment of circulation disturbance and vessel abnormalities.

A novel method to model hepatic vascular network using vessel segmentation, thinning, and completion.

The accurate modeling of the liver vessel network structure is an important prerequisite for developing a preoperative plan for the liver. Considering that extracting liver blood vessels from patient's abdominal computed tomography(CT) images requires several manual operations, this study proposed an automatic segmentation method of liver vessels based on graph cut, thinning, and vascular combination, which can obtain a complete liver vascular network. First, the CT image was preprocessed by grayscale mapping based on sigmoid function, vessel enhancement based on Hessian filter, and denoising based on anisotropic filter to enhance the grayscale contrast between the vascular and non-vascular parts of the liver. Then, the liver vessels were initially segmented based on the improved three-dimensional graph cut algorithm. Based on the obtained liver vascular structure, the vessel centerline of the liver was then extracted by the proposed thinning algorithm that continuously traversed the foreground voxel points and iteratively deleted the simple points. Finally, the combination of vascular centerline optimization was used to predict and link the vascular centerline fractured portion. The under-segmented liver vessels were complemented based on the complete vascular centerline tree. To verify the proposed hepatic vascular segmentation and complementation algorithm, the open 3D Image Reconstruction for Comparison of Algorithm Database (3Dircadb) was applied to test and quantify the results. The results showed that the proposed algorithm can accurately and effectively segment the vascular network structure from abdominal CT images, and the proposed vascular complementation method can restore the true information of under-segmented liver vessels. Graphical abstract A novel hepatic vessel segmentation method from abdominal CT images was proposed, including graph cut algorithm, centerline extraction, and broken vessel completion. First, the graph cut algorithm was used to obtain the initial segmentation result. Then, the centerline of the initial segmentation result was extracted. Finally, the initial segmentation result was optimized through centerline analysis.

An automatic evaluation method for retinal image registration based on similar vessel structure matching.

Registration of retinal images is significant for clinical diagnosis. Numerous methods have been proposed to evaluate registration performance. The available evaluation methods can work well in normal image pairs, but fair evaluation cannot be obtained for image pairs with anatomical changes. We propose an automatic method to quantitatively assess the registration of retinal images based on the extraction of similar vessel structures and modified Hausdorff distance. Firstly, vessel detection and skeletonization are performed to detect the vascular centerline. Secondly, the vessel segments having similar structures in the image pair are selected for assessment of registration. The bifurcation and terminal points are determined from the vascular centerline. Then, the Hungarian matching algorithm with a pruning process is employed to match the bifurcation and terminal points to detect similar vessel segments. Finally, a modified Hausdorff distance is employed to evaluate the performance of registration. Our experimental results show that the Pearson product-moment correlation coefficient can reach 0.76 and 0.63 in test set of normal image pairs and image pairs with anomalies respectively, which outperforms other methods. An accurate evaluation can not only compare the performance of different registration methods but also can facilitate the clinical diagnosis by screening out the inaccurate registration. Graphical abstract .

A Bifurcated Vascular Channel Construction Method based on Diploic Vein Characteristics

In skull bone tissue engineering, cells do not easily survive and proliferate in the scaffold because of the lack of nutrient transport channels. To address these problems, a vascular design and fabrication method based on human skull diploic vein characteristics was proposed. The skull sample was scanned by micro-CT, and the 3D model was constructed by Avizo. By analyzing the characteristics of the diploic vein, the vascular centerline model, path model, taper model and bifurcation model were proposed. Two vascular network examples were constructed by iteration of the bifurcation unit. The mold method of constructing vascular scaffolds embedded within the bionic channels was proposed. The scaffold material is PDMS, and the surface was coated with collagen. The Human Umbilical Vein Endothelial Cells (HUVECs) were planted into the lumen of the channels for 7 days in vitro and found to be able to proliferate. The cells cultured for three days were fluorescently stained, and it was found that the cells were well attached to the surfaces of the lumen. This vascular design and fabrication process can lay a foundation for vascularization in bone tissue engineering.

Topology Optimization Using Multiple-Possibility Fusion for Vasculature Extraction

Vascular centerline extraction from angiography images plays an important role in computer-aided diagnosis of vascular disease. To solve the common problems related to noise and inconsistent vasculatures from uneven perfusion, this paper proposes an automatic framework for accurate vascular centerline extraction from angiograms that uses multi-probability fusion-based topology optimization. In this framework, vascular region is first segmented using a learning-based method. Then, initial centerlines are obtained by applying iterative filtering operation and multi-direction indexed non-maximum suppression. Topology optimization is achieved by gap filling. A connection probability map is constructed utilizing the information of initial centerlines, texture, and orientation of vasculatures. Shortest path tracking is employed to search for optimal connections around gaps in the initial centerlines. The proposed framework is evaluated using simulative and clinical coronary angiographies. The experimental results demonstrate that the proposed method can extract centerlines with F1 score of 97.28% ± 1.2% for vasculatures in 12 clinical angiographic images. It is evident that the proposed method can extract complete and accurate vascular centerlines from angiograms and can be used to repair gaps in other filamentary structures, such as roads and retinal blood vessels. This endows our method a great potential in the analysis of filamentary structures.

Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography

Aiming at the problem that the traditional coronary artery centerline extraction method is computationally intensive and requires a large number of manual interventions, a fully automatic coronary artery centerline tracking extraction method is proposed. First, adaptive adaptation of coronary vessels was performed based on different Fresenius norms designed for coronary scale. A multi-scale differential operator is constructed based on the vascular gray-scale distribution to establish a discriminant function, thereby obtaining the initial position and tracking direction of the seed point. Then, the new ridge point and tracking direction are optimally detected within the local arc length of the initial direction to ensure that the correct ridge position can still be obtained with large error interference. Finally, the pseudo vascular centerline is removed using the vascular topology feature detection method. The experimental results show that the method can accurately extract the blood vessel center line, direction vector and other information in the coronary angiography image without manual intervention, and can be used in the computer-assisted diagnosis and treatment process of clinical cardiovascular disease.

Hybrid constraint optimization for 3D subcutaneous vein reconstruction by near-infrared images

Background and objectiveThe development of biometric identification technology and intelligent medication has enabled researchers to analyze subcutaneous veins from near-infrared images. However, the stereo reconstruction of subcutaneous veins has not been well studied, and the results are difficult to utilize in clinical practice. MethodsWe present a hybrid constraint optimization (HCO) matching algorithm for vein reconstruction to solve the matching failure problems caused by the incomplete segmentation of vein structures captured from different views. This algorithm initially introduces the existence of the epipolar and homography constraints in the subcutaneous vein matching. Then, the HCO matching algorithm of the vascular centerline is established by homography point-to-point matching, homography matrix optimization, and vascular section matching. Finally, the 3D subcutaneous vein is reconstructed on the basis of the principle of triangulation and system calibration parameters. ResultsTo validate the performance of the proposed matching method, we designed a series of experiments to evaluate the effectiveness of the hybrid constraint optimization method. The experiments were performed on simulated and real datasets. 42 real vascular images were analyzed on the basis of different matching strategies. Experimental result shows that the matching accuracy increased significantly with the proposed optimization matching method. To calculate the reconstruction accuracy, we reconstructed seven simulated cardboards and measured 10 vascular distances in each simulated cardboard. The average vascular distance error of each simulated image was within 1.0 mm, and the distance errors of 75% feature points were less than 1.5 mm. Also, we printed a 3D simulated vein model to improve the illustration of this system. The reconstruction error extends from −3.58 mm to 1.94 mm with a standard deviation of 0.68 mm and a mean of 0.07 mm. ConclusionsThe algorithm is validated in terms of homography optimization, matching efficiency, and simulated vascular reconstruction error. The experimental results demonstrate that the veins captured from the left and right views can be accurately matched through the proposed algorithm.

Automatic centerline extraction of cerebrovascular in 4D CTA based on tubular features

Vascular centerlines have crucial significance in reconstruction, registration, segmentation and vascular parameter analysis. The extraction of vessel structures remains a difficult problem in the completeness and continuity of results. In this paper, we present a novel method to extract cerebrovascular centerlines from four-dimensional computed tomography angiography images. Tubular features and vascular directions are used to extract initial centerlines, and the offset correction is introduced in the vascular orthogonal plane. In addition, we also present a post-processing method to connect interruptions of centerlines. We perform a quantitative validation using clinical images and public data sets of MRA brain images. Our experimental results demonstrate that the proposed algorithm not only shows higher accuracy in complicated vessel structures, but also outperforms previous approaches in terms of high validity and universality.

Treatment Effect of Balloon Pulmonary Angioplasty in Chronic Thromboembolic Pulmonary Hypertension Quantified by Automatic Comparative Imaging in Computed Tomography Pulmonary Angiography.

Balloon pulmonary angioplasty (BPA) in patients with inoperable chronic thromboembolic pulmonary hypertension (CTEPH) can have variable outcomes. To gain more insight into this variation, we designed a method for visualizing and quantifying changes in pulmonary perfusion by automatically comparing computed tomography (CT) pulmonary angiography before and after BPA treatment. We validated these quantifications of perfusion changes against hemodynamic changes measured with right-sided heart catheterization. We studied 14 consecutive CTEPH patients (12 women; age, 70.5 ± 24), who underwent CT pulmonary angiography and right-sided heart catheterization, before and after BPA. Posttreatment images were registered to pretreatment CT scans (using the Elastix toolbox) to obtain corresponding locations. Pulmonary vascular trees and their centerlines were detected using a graph cuts method and a distance transform method, respectively. Areas distal from vessels were defined as pulmonary parenchyma. Subsequently, the density changes within the vascular centerlines and parenchymal areas were calculated and corrected for inspiration level differences. For visualization, the densitometric changes were displayed in color-coded overlays. For quantification, the median and interquartile range of the density changes in the vascular and parenchymal areas (ΔVD and ΔPD) were calculated. The recorded changes in hemodynamic parameters, including changes in systolic, diastolic, and mean pulmonary artery pressure (ΔsPAP, ΔdPAP, and ΔmPAP, respectively) and vascular resistance (ΔPVR), were used as reference assessments of the treatment effect. Spearman correlation coefficients were employed to investigate the correlations between changes in perfusion and hemodynamic changes. Comparative imaging maps showed distinct patterns in perfusion changes among patients. Within pulmonary vessels, the interquartile range of ΔVD correlated significantly with ΔsPAP (R = -0.58, P = 0.03), ΔdPAP (R = -0.71, P = 0.005), ΔmPAP (R = -0.71, P = 0.005), and ΔPVR (R = -0.77, P = 0.001). In the parenchyma, the median of ΔPD had significant correlations with ΔdPAP (R = -0.58, P = 0.030) and ΔmPAP (R = -0.59, P = 0.025). Comparative imaging analysis in CTEPH patients offers insight into differences in BPA treatment effect. Quantification of perfusion changes provides noninvasive measures that reflect hemodynamic changes.

Artificial intelligence and cardiovascular computed tomography

Cardiovascular computed tomography (CT) routinely acquires volumetric datasets with large numbers of axial slices. While review of the axial slices remains an important part of the image analysis, a critical step is detailed reconstruction of multiple oblique planes and volumes on an advanced 3D workstation (1). This is performed by a combination of manual and (semi)-automated reconstruction along oblique planes, vascular centerlines, and volumes. This analysis results in a large number of discrete qualitative and quantitative data-points and is increasingly supported by dedicated, “smart” software.

A monocentric centerline extraction method for ring-like blood vessels.

Centerline is generally used to measure topological and morphological parameters of blood vessels, which is pivotal for the quantitative analysis of vascular diseases. However, previous centerline extraction methods have two drawbacks on complex blood vessels, represented as the failure on ring-like structures and the existing of multi-voxel width. In this paper, we propose a monocentric centerline extraction method for ring-like blood vessels, which consists of three components. First, multiple centerlines are generated from the seed points that are chosen by randomly sprinkling points on blood vessel data. Second, multi-centerline fusion is used to repair the notches of centerlines on ring-like vessels, and the local maximum of distance from oundary is employed to remedy the missing centerline points. Finally, monocentric processing is devised to keep the vascular centerline with single voxel width. We compared the proposed method with Wan et al.'s method and topological thinning on five groups of data including synthesized vascular datasets and MR brain images. The result showed the proposed method performed better than the two contrast methods both by visual inspection and by quantitative assessment, which demonstrated the performance of the proposed method on ring-like blood vessels as well as the elimination of multi-voxel width points.

An automatic and efficient coronary arteries extraction method in CT angiographies

Abstract Coronary artery disease (CAD) is one of the most leading cause of death in the world recently. Although conventional coronary angiography (CCA) is still as a gold standard for diagnosis of CAD, coronary CT angiography (CTA) is widely used due to its non-invasion. Meanwhile, centerline extraction of the coronary arteries offers some essential information for the radiologists. However, their extractions are extremely difficult due to the CT imaging nature, such as strong motion artifacts, poor contrast injection timing and low contrast. In addition, as the data increases rapidly, manual extraction becomes impractical, a fully automatic extraction method is becoming more necessary. In this paper, we propose an automatic and efficient method to extract the vascular centerlines in CTA. Firstly, the heart region is isolated through chest wall and spine removal based on CT thresholding and DBSCAN clustering. Secondly, real coronary arteries are distinguished from artifacts via an identification function based on their different anisotropic distributions of Frangi's vesselness. And good heart isolation and artifact removal can avoid the detection of the aorta and ostium always used in other works. Thirdly, we study a directional connectedness-related clustering method to cluster every vascular segments for forming a reasonable vascular tree, as well as our method has ability at collateral vessel's elimination in refinement step according to actual demand. Fourthly, in order to handle some break-offs and holes in post-enhanced image, centerlines are extracted by employing a robust method based on principle curves. Finally, the performance of our method is evaluated on RCAA datasets in CTA. Our method performs well followed by a comparison of the-state-of-the-art methods.

Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation

Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation. The algorithm consists of four levels, two of which detect healthy arteries of varying sizes and two that specialize in different types of vascular pathology: severe calcification and occlusion. We perform four main steps at each level: appropriate parameters for each level are selected automatically, a set of centrally located voxels is detected, these voxels are connected together based on the connection criteria, and the resulting centerline tree is corrected from spurious branches. The proposed method was tested on 25 CTA scans of the lower limbs, achieving an average overlap rate of 89% and an average detection rate of 82%. The average execution time using four CPU cores was 70s, and the technique was successful also in detecting very distal artery branches, e.g., in the foot.