Segmentation Of Cerebral Vessels Research Articles

Robust data-driven segmentation of pulsatile cerebral vessels using functional magnetic resonance imaging.

Functional magnetic resonance imaging (fMRI) captures rich physiological and neuronal information that can offer insights into neurofluid dynamics, vascular health, and waste clearance function. The availability of cerebral vessel segmentation could facilitate fluid dynamics research in fMRI. However, without magnetic resonance angiography scans, cerebral vessel segmentation is challenging and time-consuming. This study leverages cardiac-induced pulsatile fMRI signal to develop a data-driven, automatic segmentation of large cerebral arteries and the superior sagittal sinus (SSS). The method was validated in a local dataset by comparing it to ground truth cerebral artery and SSS segmentations. Using the Human Connectome Project (HCP) aging dataset, the method's reproducibility was tested on 422 participants aged 36 to 100 years, each with four repeated fMRI scans. The method demonstrated high reproducibility, with an intraclass correlation coefficient > 0.7 in both cerebral artery and SSS segmentation volumes. This study demonstrates that the large cerebral arteries and SSS can be reproducibly and automatically segmented in fMRI datasets, facilitating the investigation of fluid dynamics in these regions.

Multiscale attention network via topology learning for cerebral vessel segmentation in angiography images

Cerebrovascular diseases are a widespread threat to human health. The accurate extraction of cerebral vessel structures is of paramount importance in the diagnosis and treatment of cerebrovascular diseases. However, the complexity of cerebral vessel structures and the low imaging contrast present significant challenges for vessel segmentation. Therefore, we propose a Multiscale Attention Network based on topological learning to extract vessel structures from angiographic images. This method employs a Multiscale Squeeze Attention (MSA) module for channel-wise attention learning, extracting multiscale attention feature maps from angiographic images. To maintain the topological connectivity of vessel segmentation, we introduced the clDice loss function to enforce skeleton connectivity of vessel segmentation. We conducted an experimental analysis of the proposed method using a publicly available cerebral vessel dataset. The results demonstrated that the proposed method achieved a sensitivity score of 0.8507 and a dice score of 0.8669 for cerebrovascular segmentation, enabling accurate and complete extraction of vascular structures. The proposed method was extended to coronary angiography images. The results show that the proposed method can accurately extract coronary structures, proving its broad applicability to other vascular segmentation tasks.

ERNet: Edge Regularization Network for Cerebral Vessel Segmentation in Digital Subtraction Angiography Images.

Stroke is a leading cause of disability and fatality in the world, with ischemic stroke being the most common type. Digital Subtraction Angiography images, the gold standard in the operation process, can accurately show the contours and blood flow of cerebral vessels. The segmentation of cerebral vessels in DSA images can effectively help physicians assess the lesions. However, due to the disturbances in imaging parameters and changes in imaging scale, accurate cerebral vessel segmentation in DSA images is still a challenging task. In this paper, we propose a novel Edge Regularization Network (ERNet) to segment cerebral vessels in DSA images. Specifically, ERNet employs the erosion and dilation processes on the original binary vessel annotation to generate pseudo-ground truths of False Negative and False Positive, which serve as constraints to refine the coarse predictions based on their mapping relationship with the original vessels. In addition, we exploit a Hybrid Fusion Module based on convolution and transformers to extract local features and build long-range dependencies. Moreover, to support and advance the open research in the field of ischemic stroke, we introduce FPDSA, the first pixel-level semantic segmentation dataset for cerebral vessels. Extensive experiments on FPDSA illustrate the leading performance of our ERNet.

High-throughput 3DRA segmentation of brain vasculature and aneurysms using deep learning

Background and Objectives:Automatic segmentation of the cerebral vasculature and aneurysms facilitates incidental detection of aneurysms. The assessment of aneurysm rupture risk assists with pre-operative treatment planning and enables in-silico investigation of cerebral hemodynamics within and in the vicinity of aneurysms. However, ensuring precise and robust segmentation of cerebral vessels and aneurysms in neuroimaging modalities such as three-dimensional rotational angiography (3DRA) is challenging. The vasculature constitutes a small proportion of the image volume, resulting in a large class imbalance (relative to surrounding brain tissue). Additionally, aneurysms and vessels have similar image/appearance characteristics, making it challenging to distinguish the aneurysm sac from the vessel lumen.Methods:We propose a novel multi-class convolutional neural network to tackle these challenges and facilitate the automatic segmentation of cerebral vessels and aneurysms in 3DRA images. The proposed model is trained and evaluated on an internal multi-center dataset and an external publicly available challenge dataset.Results:On the internal clinical dataset, our method consistently outperformed several state-of-the-art approaches for vessel and aneurysm segmentation, achieving an average Dice score of 0.81 (0.15 higher than nnUNet) and an average surface-to-surface error of 0.20 mm (less than the in-plane resolution (0.35 mm/pixel)) for aneurysm segmentation; and an average Dice score of 0.91 and average surface-to-surface error of 0.25 mm for vessel segmentation. In 223 cases of a clinical dataset, our method accurately segmented 190 aneurysm cases.Conclusions:The proposed approach can help address class imbalance problems and inter-class interference problems in multi-class segmentation. Besides, this method performs consistently on clinical datasets from four different sources and the generated results are qualified for hemodynamic simulation. Code available at https://github.com/cistib/vessel-aneurysm-segmentation.

Deep Learning Based Real-Time Semantic Segmentation of Cerebral Vessels and Cranial Nerves in Microvascular Decompression Scenes.

Automatic extraction of cerebral vessels and cranial nerves has important clinical value in the treatment of trigeminal neuralgia (TGN) and hemifacial spasm (HFS). However, because of the great similarity between different cerebral vessels and between different cranial nerves, it is challenging to segment cerebral vessels and cranial nerves in real time on the basis of true-color microvascular decompression (MVD) images. In this paper, we propose a lightweight, fast semantic segmentation Microvascular Decompression Network (MVDNet) for MVD scenarios which achieves a good trade-off between segmentation accuracy and speed. Specifically, we designed a Light Asymmetric Bottleneck (LAB) module in the encoder to encode context features. A Feature Fusion Module (FFM) was introduced into the decoder to effectively combine high-level semantic features and underlying spatial details. The proposed network has no pretrained model, fewer parameters, and a fast inference speed. Specifically, MVDNet achieved 76.59% mIoU on the MVD test set, has 0.72 M parameters, and has a 137 FPS speed using a single GTX 2080Ti card.

Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation.

Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal–noise ratio and the relatively small sizes, segmenting the cerebral vessels in LSCI has always been a technical challenge. Recently, deep learning has shown its advantages in vascular segmentation. Nonetheless, ground truth by manual labeling is usually required for training the network, which makes it difficult to implement in practice. In this manuscript, we proposed a deep learning-based method for real-time cerebral vessel segmentation of LSCI without ground truth labels, which could be further integrated into intraoperative blood vessel imaging system. Synthetic LSCI images were obtained with a synthesis network from LSCI images and public labeled dataset of Digital Retinal Images for Vessel Extraction, which were then used to train the segmentation network. Using matching strategies to reduce the size discrepancy between retinal images and laser speckle contrast images, we could further significantly improve image synthesis and segmentation performance. In the testing LSCI images of rodent cerebral vessels, the proposed method resulted in a dice similarity coefficient of over 75%.

Generative adversarial network based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image

The accurate segmentation of cerebral vessels from time-of-flight magnetic resonance angiography (TOF-MRA) data is crucial for the diagnosis and treatment of cerebrovascular diseases. However, cerebrovascular segmentation remains challenging due to the tiny and complex structure of the vessels. This paper proposed a GAN-based deep-based method that maps the TOF-MRA data to 3D cerebral vessels. To improve the capability of feature expression, the proposed network integrates squeeze-and-excite blocks into a Vnet structure to extract and accumulate features at different scales. In addition, we introduce adversarial loss for network training, which can help the model to learn the distribution of the cerebral vessels. Besides, we introduce island number and minimum diameter to evaluate the spatial consistency and recognized minimal vessel of the cerebrovascular segmentation results. The proposed method is validated on 102 TOF-MRA cases with manually annotated labels. The experimental results indicate that our method can identify more tiny cerebral vessels meanwhile maintaining the spatial consistency of the cerebral vessel structure. Besides, our GAN-based cerebrovascular segmentation method reaches an average dice similar coefficient of 89.89%, outperforming other state-of-the-art segmentation methods.

An evaluation of performance measures for arterial brain vessel segmentation

BackgroundArterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation.MethodsTo simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient.ResultsThe distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation.ConclusionsAverage Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches.

Cerebrovascular segmentation from TOF-MRA based on multiple-U-net with focal loss function

Background and ObjectiveAccurate cerebrovascular segmentation plays an important role in the diagnosis of cerebrovascular diseases. Considering the complexity and uncertainty of doctors' manual segmentation of cerebral vessels, this paper proposed an automatic segmentation algorithm based on Multiple-U-net (M-U-net) to segment cerebral vessel structures from the Time-of-flight Magnetic Resonance Angiography (TOF-MRA) data. MethodsFirst, the TOF-MRA data was normalized by volume and then divided into three groups through slices of axial, coronal and sagittal directions respectively. Three single U-nets were trained by divided dataset. To solve the problem of uneven distribution of positive and negative samples, the focal loss function was adopted in training. After obtaining the prediction results of three single U-nets, the voting feature fusion and the post-processing process based on connected domain analysis would be performed. 95 volumes of TOF-MRA provided by the MIDAS platform were applied to the experiment, among which 20 volumes were treated as the training dataset, 5 volumes were used as the validation dataset and the remaining 70 volumes were divided into 10 groups to test the trained model respectively. ResultsExperiments showed that the proposed M-U-net based algorithm achieved 88.60% and 87.93% Dice Similarity Coefficient (DSC) on the verification dataset and testing dataset, which performed better than any single U-net. ConclusionsCompared with other existing algorithms, our algorithm reached the state of the art level. The feature fusion of three single U-nets could effectively complement the segmentation results.

On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking

Average Hausdorff distance is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, it is used to compare ground truth images with segmentations allowing their ranking. We identified, however, ranking errors of average Hausdorff distance making it less suitable for applications in segmentation performance assessment. To mitigate this error, we present a modified calculation of this performance measure that we have coined “balanced average Hausdorff distance”. To simulate segmentations for ranking, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation as our use-case. Adding the created errors consecutively and randomly to the ground truth, we created sets of simulated segmentations with increasing number of errors. Each set of simulated segmentations was ranked using both performance measures. We calculated the Kendall rank correlation coefficient between the segmentation ranking and the number of errors in each simulated segmentation. The rankings produced by balanced average Hausdorff distance had a significantly higher median correlation (1.00) than those by average Hausdorff distance (0.89). In 200 total rankings, the former misranked 52 whilst the latter misranked 179 segmentations. Balanced average Hausdorff distance is more suitable for rankings and quality assessment of segmentations than average Hausdorff distance.

Three-dimensional segmentation and symbolic representation of cerebral vessels on 3DRA images of arteriovenous malformations

BackgroundEndovascular embolization is a minimally invasive interventional method for the treatment of neurovascular pathologies such as aneurysms, arterial stenosis or arteriovenous malformations (AVMs). In this context, neuroradiologists need efficient tools for interventional planning and microcatheter embolization procedures optimization. Thus, the development of helpful methods is necessary to solve this challenging issue. MethodsA complete pipeline aiming to assist neuroradiologists in the visualization, interpretation and exploitation of three-dimensional rotational angiographic (3DRA) images for interventions planning in case of AVM is proposed. The developed method consists of two steps. First, an automated 3D region-based segmentation of the cerebral vessels which feed and drain the AVM is performed. From this, a graph-like tree representation of these connected vessels is then built. This symbolic representation provides a vascular network modelization with hierarchical and geometrical features that helps in the understanding of the complex angioarchitecture of the AVM. ResultsThe developed workflow achieves the segmentation of the vessels and of the malformation. It improves the 3D visualization of this complex network and highlights its three main components that are the arteries, the veins and the nidus. The symbolic representation then brings a better comprehension of the vessels angioarchitecture. It provides decomposition into topologically related vessels, offering the possibility to reduce the complexity due to the malformed vessels and also determine the optimal paths for AVM embolization during interventions planning. ConclusionsA relevant vascular network modelization has been developed that constitutes a breakthrough in the assistance of neuroradiologists for AVM endovascular embolization planning.

An Active Contour Model Based on Adaptive Threshold for Extraction of Cerebral Vascular Structures.

Cerebral vessel segmentation is essential and helpful for the clinical diagnosis and the related research. However, automatic segmentation of brain vessels remains challenging because of the variable vessel shape and high complex of vessel geometry. This study proposes a new active contour model (ACM) implemented by the level-set method for segmenting vessels from TOF-MRA data. The energy function of the new model, combining both region intensity and boundary information, is composed of two region terms, one boundary term and one penalty term. The global threshold representing the lower gray boundary of the target object by maximum intensity projection (MIP) is defined in the first-region term, and it is used to guide the segmentation of the thick vessels. In the second term, a dynamic intensity threshold is employed to extract the tiny vessels. The boundary term is used to drive the contours to evolve towards the boundaries with high gradients. The penalty term is used to avoid reinitialization of the level-set function. Experimental results on 10 clinical brain data sets demonstrate that our method is not only able to achieve better Dice Similarity Coefficient than the global threshold based method and localized hybrid level-set method but also able to extract whole cerebral vessel trees, including the thin vessels.

Phase contrast MR imaging measurements of blood flow in healthy human cerebral vessel segments

Phase contrast (PC) magnetic resonance imaging was used to obtain velocity measurements in 30 healthy subjects to provide an assessment of hemodynamic parameters in cerebral vessels. We expect a lower coefficient-of-variation (COV) of the volume flow rate (VFR) compared to peak velocity (vpeak) measurements and the COV to increase in smaller caliber arteries compared to large arteries.PC velocity maps were processed to calculate vpeak and VFR in 26 vessel segments. The mean, standard deviation and COV, of vpeak and VFR in each segment were calculated. A bootstrap-style analysis was used to determine the minimum number of subjects required to accurately represent the population. Significance of vpeak and VFR asymmetry was assessed in 10 vessel pairs.The bootstrap analysis suggested that averaging more than 20 subjects would give consistent results. When averaged over the subjects, vpeak and VFR ranged from 5.2 ± 7.1 cm s−1, 0.41 ± 0.58 ml s−1 (in the anterior communicating artery; mean ± standard deviation) to 73 ± 23 cm s−1, 7.6 ± 1.7 ml s−1 (in the left internal carotid artery), respectively. A tendency for VFR to be higher in the left hemisphere was observed in 88.8% of artery pairs, while the VFR in the right transverse sinus was larger. The VFR COV was larger than vpeak COV in 57.7% of segments, while smaller vessels had higher COV.Significance and potential impact: VFR COV was not generally higher than vpeak COV. COV was higher in smaller vessels as expected. These summarized values provide a base against which vpeak and VFR in various disease states can be compared.

Extraction of vessel networks based on multiview projection and phase field model

The precise segmentation of cerebral vessels is essential for detecting cerebral diseases. This work proposed a novel method for automatic extraction of blood vessels. The proposed framework includes three steps: (a) projection from 3D volume to 2D plane: in order to make up the small percentage vessels occupy in each slice and to avoid overlapping between vessels, the volume dataset is projected from different directions; (b) extraction on 2D plane: a new energy model is proposed using phase-field and statistical information which is based on the Allen–Cahn equation with a double well potential and statistical data fitting terms. The segmentation is based on curve evolution. This model is effective in extracting blood vessels with low contrast, multi-branch structure and intensity inhomogeneity from projection images; (c) projecting back from 2D plane to 3D volume: the pixels segmented from previous step will be projected back into the volume dataset, and the corresponding voxels in the volume will be reserved to construct the blood vessels in three-dimensional space. Experimental results illustrate that the performance of the methods is better than existing techniques.

Segmentation and reconstruction of cerebral vessels from 3D rotational angiography for AVM embolization planning.

Diagnosis and computer-guided therapy of cerebral Arterio-Venous Malformations (AVM) require an accurate understanding of the cerebral vascular network both from structural and biomechanical point of view. We propose to obtain such information by analyzing three Dimensional Rotational Angiography (3DRA) images. In this paper, we describe a two-step process allowing 1) the 3D automatic segmentation of cerebral vessels from 3DRA images using a region-growing based algorithm and 2) the reconstruction of the segmented vessels using the 3D constrained Delaunay Triangulation method. The proposed algorithm was successfully applied to reconstruct cerebral blood vessels from ten datasets of 3DRA images. This software allows the neuroradiologist to separately analyze cerebral vessels for pre-operative interventions planning and therapeutic decision making.

Separation of cortical arteries and veins in optical neurovascular imaging

Separation of arteries and veins in the cerebral cortex is of significant importance in the studies of cortical hemodynamics, such as the changes of cerebral blood flow, perfusion or oxygen concentration in arteries and veins under different pathological and physiological conditions. Yet the cerebral vessel segmentation and vessel-type separation are challenging due to the complexity of cortical vessel characteristics and low spatial signal-to-noise ratio. In this work, we presented an effective full-field method to differentiate arteries and veins in cerebral cortex using dual-modal optical imaging technology including laser speckle imaging (LSI) and optical intrinsic signals (OIS) imaging. The raw contrast images were acquired by LSI and processed with enhanced laser speckle contrast analysis (eLASCA) algorithm. The vascular pattern was extracted and segmented using region growing algorithm from the eLASCA-based LSI. Meanwhile, OIS images were acquired alternatively with 630 and 870 nm to obtain an oxyhemoglobin concentration map over cerebral cortex. Then the separation of arteries and veins was accomplished by Otsu threshold segmentation algorithm based on the OIS information and segmentation of LSI. Finally, the segmentation and separation performances were assessed using area overlap measure (AOM). The segmentation and separation of cerebral vessels in cortical optical imaging have great potential applications in full-field cerebral hemodynamics monitoring and pathological study of cerebral vascular diseases, as well as in clinical intraoperative monitoring.

Segmentation of brain magnetic resonance angiography images based on MAP–MRF with multi-pattern neighborhood system and approximation of regularization coefficient

Existing maximum a posteriori probability and Markov random field (MRF) models have limitations associated with: (1) the ordinary neighborhood system being unable to differentiate subtle changes due to several-to-one correspondence within the neighborhood; and (2) difficulty finding an appropriate parameter to balance between the spatial context and the data likelihood. Aiming at overcoming the limitations and applications to segmentation of cerebral vessels from magnetic resonance angiography images, we have proposed (1) a multi-pattern neighborhood system and corresponding energy equation to enable the MRF model for segmenting fine cerebral vessels with complicated context; and (2) an iterative approximation algorithm based on the maximum pseudo-likelihood and the space coding mode for the automatic parameter estimation of high level model of MRF. In the implementation, two computational strategies have been employed to speed up: the candidate space of cerebral vessels based on a threshold of the response to multi-scale filtering, and parallel computation of major equations. Three phantoms simulating segmentation challenges of vessels have been devised to quantitatively validate the algorithm. In addition, 10 three-dimensional clinical data sets have been used to validate the algorithm qualitatively. It has been shown that the proposed method could yield smaller error, improve the spatial resolution of MRF model, and better balance the smoothing and data likelihood than the traditional trial-and-error estimation. Comparative studies have shown that the proposed method is better than the 3 segmentation algorithms (Hassouna et al., 2006; Hao et al., 2008; Gao et al., 2011) in terms of segmentation accuracy, robustness to noise and varying curvatures as well as radii.

Multi-Branched Blood Vessels Segmentation Based on Phase-Field and Statistical Model

The precise segmentation of cerebral vessels is essential for the detection of cerebrovascular diseases. The complex structures of cerebral vessels and the low contranst of thin vessels in medical images make precise segmentation difficult. In this study, we propose a new phase-field and statistical model for blood vessel segmentation. The proposed model is based on the Allen-Chan equation with double well potential and statistical distribution function. The brain tissues in the image are modeled by Gaussian distribution while cerebral vessels are modeled by uniform distribution respectively. The region distribution information combined with the phase-field model is used in curve evolution. And the level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation. Comparisons with the LBF model and LCV model show that our model can achieve better results with fewer iteration number and less time.

A Statistical Cerebroarterial Atlas Derived from 700 MRA Datasets

The cerebroarterial system is a complex network of arteries that supply the brain cells with vitally important nutrients and oxygen. The inter-individual differences of the cerebral arteries, especially at a finer level, are still not understood sufficiently. The aim of this work is to present a statistical cerebroarterial atlas that can be used to overcome this problem. Overall, 700 Time-of-Flight (TOF) magnetic resonance angiography (MRA) datasets of healthy subjects were used for atlas generation. Therefore, the cerebral arteries were automatically segmented in each dataset and used for a quantification of the vessel diameters. After this, each TOF MRA dataset as well as the corresponding vessel segmentation and vessel diameter dataset were registered to the MNI brain atlas. Finally, the registered datasets were used to calculate a statistical cerebroarterial atlas that incorporates information about the average TOF intensity, probability for a vessel occurrence and mean vessel diameter for each voxel. Visual analysis revealed that arteries with a diameter as small as 0.5 mm are well represented in the atlas with quantitative values that are within range of anatomical reference values. Moreover, a highly significant strong positive correlation between the vessel diameter and occurrence probability was found. Furthermore, it was shown that an intensity-based automatic segmentation of cerebral vessels can be considerable improved by incorporating the atlas information leading to results within the range of the inter-observer agreement. The presented cerebroarterial atlas seems useful for improving the understanding about normal variations of cerebral arteries, initialization of cerebrovascular segmentation methods and may even lay the foundation for a reliable quantification of subtle morphological vascular changes.

A Fast and Fully Automatic Method for Cerebrovascular Segmentation on Time-of-Flight (TOF) MRA Image

The precise three-dimensional (3-D) segmentation of cerebral vessels from magnetic resonance angiography (MRA) images is essential for the detection of cerebrovascular diseases (e.g., occlusion, aneurysm). The complex 3-D structure of cerebral vessels and the low contrast of thin vessels in MRA images make precise segmentation difficult. We present a fast, fully automatic segmentation algorithm based on statistical model analysis and improved curve evolution for extracting the 3-D cerebral vessels from a time-of-flight (TOF) MRA dataset. Cerebral vessels and other tissue (brain tissue, CSF, and bone) in TOF MRA dataset are modeled by Gaussian distribution and combination of Rayleigh with several Gaussian distributions separately. The region distribution combined with gradient information is used in edge-strength of curve evolution as one novel mode. This edge-strength function is able to determine the boundary of thin vessels with low contrast around brain tissue accurately and robustly. Moreover, a fast level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation. Quantitative comparisons with 10 sets of manual segmentation results showed that the average volume sensitivity, the average branch sensitivity, and average mean absolute distance error are 93.6%, 95.98%, and 0.333 mm, respectively. By applying the algorithm to 200 clinical datasets from three hospitals, it is demonstrated that the proposed algorithm can provide good quality segmentation capable of extracting a vessel with a one-voxel diameter in less than 2 min. Its accuracy and speed make this novel algorithm more suitable for a clinical computer-aided diagnosis system.