Centerline Of Blood Vessel Research Articles

Three-dimensional vascular graph construction from depth information of blood vessel centerlines in optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique for visualizing high-resolution volumetric vascular networks. Although OCTA has been widely employed in vascular network analysis, most studies have been limited to the analysis of two-dimensional (2D) en-face projection images because leveraging the full potential of OCTA’s three-dimensional (3D) information has been challenging due to projection artifacts beneath blood vessels. In this study, we propose a semi-automatic method for constructing a 3D vascular graph from 3D OCTA images without relying on data-driven learning strategies such as deep learning. The proposed method estimates the depth information of blood vessel centerlines in 2D en-face images and constructs a 3D vascular graph by integrating the depth estimation results for all vessel centerline segments. We demonstrate the effectiveness of the proposed method through experiments conducted on both simulated datasets and real datasets acquired from the dorsal dermis of mice.

Automatic Method for Blood Vessel Diameter Measurement on Color Retinal Images

Automatic detection of retinal blood vessels and measurement of blood vessel diameter has become very useful in medical applications, including the diagnosis and monitoring of vascular therapy. This paper present a new method for the detection of retinal blood vessels from fundus images. In addition, it accurately determines the blood vessels diameter. The proposed system consists of several stages starting from the detection of a complete blood vessel network (retinal images), a segmentation process, an extraction of blood vessel centerlines, and a new approach for the measurement of the blood vessel diameters utilizing the circle diameter approach. The application of the proposed method is based on three publicly available databases (DRIVE, STARE, CHASE_DB and HRF), which has been conducted and compared to recently reported measurement techniques. Numerical results demonstrate the efficacy of the proposed method achieving a success rate of 100% with 2.2454×10−4 as mean and 4.5833×10−5 as standard deviation differences. This could be harnessed in the future blood vessel measurement techniques.

MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction

Automatic segmentation and centerline extraction of blood vessels from retinal fundus images is an essential step to measure the state of retinal blood vessels and achieve the goal of auxiliary diagnosis. Combining the information of blood vessel segments and centerline can help improve the continuity of results and performance. However, previous studies have usually treated these two tasks as separate research topics. Therefore, we propose a novel multitask learning network (MSC-Net) for retinal vessel segmentation and centerline extraction. The network uses a multibranch design to combine information between two tasks. Channel and atrous spatial fusion block (CAS-FB) is designed to fuse and correct the features of different branches and different scales. The clDice loss function is also used to constrain the topological continuity of blood vessel segments and centerline. Experimental results on different fundus blood vessel datasets (DRIVE, STARE, and CHASE) show that our method can obtain better segmentation and centerline extraction results at different scales and has better topological continuity than state-of-the-art methods.

MRI-based comprehensive analysis of vascular anatomy and hemodynamics.

Standardized methods for mapping the complex blood flow in vessels are essential for processing the large data volume acquired from 4D Flow MRI. We present a method for systematic and efficient analysis of anatomy and flow in large human blood vessels. To attain the best outcomes in cardiac surgery, vascular modifications that lead to secondary flow patterns such as vortices should be avoided. In this work, attention was paid to the undesired cancelation of vortices with opposite directions of rotation, known as Dean flow patterns, using hemodynamic parameters such as circulation and helicity density. Our approach is based on the multiplanar reconstruction (MPR) of a multi-dimensional feature-space along the blood vessel's centerline. Hemodynamic parameters and anatomic information were determined in-plane from the reconstructed feature-space and from the blood vessel's centerline. A modified calculation of circulation and helicity density and novel parameters for quantifying Dean flow were developed. To test the model performance, we applied our methods to three test cases. Comprehensive information on position, magnitude and interrelation of vascular anatomy and hemodynamics were extracted from 4D Flow MRI datasets. The results show that the Dean flow patterns can be efficiently assessed using the novel parameters. Our approach to comprehensively and simultaneously quantify multiple parameters of vascular anatomy and hemodynamics from 4D Flow MRI provides new insights to map complex hemodynamic conditions.

Development of a New Image-Guided Neuronavigation System: Mixed-Reality Projection Mapping Is Accurate and Feasible.

Image-guided systems improve the safety, functional outcome, and overall survival of neurosurgery but require extensive equipment. To develop an image-guided surgery system that combines the brain surface photographic texture (BSP-T) captured during surgery with 3-dimensional computer graphics (3DCG) using projection mapping. Patients who underwent initial surgery with brain tumors were prospectively enrolled. The texture of the 3DCG (3DCG-T) was obtained from 3DCG under similar conditions as those when capturing the brain surface photographs. The position and orientation at the time of 3DCG-T acquisition were used as the reference. The correct position and orientation of the BSP-T were obtained by aligning the BSP-T with the 3DCG-T using normalized mutual information. The BSP-T was combined with and displayed on the 3DCG using projection mapping. This mixed-reality projection mapping (MRPM) was used prospectively in 15 patients (mean age 46.6 yr, 6 males). The difference between the centerlines of surface blood vessels on the BSP-T and 3DCG constituted the target registration error (TRE) and was measured in 16 fields of the craniotomy area. We also measured the time required for image processing. The TRE was measured at 158 locations in the 15 patients, with an average of 1.19±0.14 mm (mean±standard error). The average image processing time was 16.58 min. Our MRPM method does not require extensive equipment while presenting information of patients' anatomy together with medical images in the same coordinate system. It has the potential to improve patient safety.

Analysis of the effect of guidewire position on stenosis diagnosis using computational fluid dynamics

BackgroundFractional flow reserve is an accurate method for diagnosing stenosis. The difficulty in using this procedure lies in placing the guidewire precisely at the blood vessel centerline. Owing to the long distance between the insertion point and the stenosis, a guidewire inclination can occur. Therefore, the main objective of this study is to investigate how the measured pressure in a blood vessel varies with the guidewire position. MethodsA three-dimensional model of blood flow is developed and numerically simulated. Two positions and two inclination angles from the blood vessel centerline and three throat diameters are investigated. The predicted results are validated using the available experimental data. The predicted results and actual measurements are observed to agree well with each other. ResultsThe pressure drop coefficient (CDP) increases because of guidewire insertion. When the guidewire is placed at inclined positions in moderate stenosis, the values of CDP are 66 and 68, depending on the inclination angle; the errors in CDP are 69% and 76%, respectively. At a high flow rate, the errors are reduced to 67% and 70%, respectively. The error in the CDP ranges from 42% to 61% when the guidewire is placed parallel to the centerline. For severe stenosis, the CDP is nearly the same at all positions and varies between 240 and 250; without a guidewire, the CDP is 163. ConclusionsThe findings confirmed that practitioners should be aware of the guidewire position during the operation. The displacement of the guidewire should be estimated, and the corresponding error must be considered.

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.

Retrieving challenging vessel connections in retinal images by line co-occurrence statistics

Natural images contain often curvilinear structures, which might be disconnected, or partly occluded. Recovering the missing connection of disconnected structures is an open issue and needs appropriate geometric reasoning. We propose to find line co-occurrence statistics from the centerlines of blood vessels in retinal images and show its remarkable similarity to a well-known probabilistic model for the connectivity pattern in the primary visual cortex. Furthermore, the probabilistic model is trained from the data via statistics and used for automated grouping of interrupted vessels in a spectral clustering based approach. Several challenging image patches are investigated around junction points, where successful results indicate the perfect match of the trained model to the profiles of blood vessels in retinal images. Also, comparisons among several statistical models obtained from different datasets reveal their high similarity, i.e., they are independent of the dataset. On top of that the best approximation of the statistical model with the symmetrized extension of the probabilistic model on the projective line bundle is found with a least square error smaller than 2%. Apparently, the direction process on the projective line bundle is a good continuation model for vessels in retinal images.

A robust construction algorithm of the centerline skeleton for complex aortic vascular structure using computational fluid dynamics

Centerlines of blood vessels are useful tools to make important anatomical measurements (length, diameter, area), which cannot be accurately obtained using 2D images. In this paper a brand new method for centerline extraction of vascular trees is presented. By using computational fluid dynamics (CFD) we are able to obtain a robust and purely functional centerline allowing us to support better measurements than classic purely geometrical-based centerlines. We show that the CFD-based centerline is within a few pixels from the geometrical centerline where the latter is defined (far away from inlet/outlets and from the branches). We show that the centerline computed with our method is not affected by traditional errors of other classical volume-based algorithms such as topological thinning, and could be a potential alternative to be considered for future studies.

Analysis of rotation angles and motions of the flexible mechanisms in bifurcated blood vessels.

This article investigates the relationship of rotation angle of two ends of elastic guidewires, and motion method of guidewires and catheters in bifurcated blood vessels. The relationship of rotation angles of the two ends of the guidewire has first been studied by mathematical reasoning, indicating that the rotation angle of the front end of guidewire can be precisely described by that of the rear end. In order to satisfy the requirements of different vascular interventional surgeries, we further investigated a general model of bifurcated human blood vessels. Based on this model, a new motion method, which ensures the guidewire and catheter move cooperatively along center lines of blood vessels to the final target point, has been introduced. Rotation angles of the guidewire in this model were then analyzed. By experiments to verify the functional relationship between rotation angles of two ends of guidewire, the motion method and algorithm of guidewire and catheter are correct. Our study provides references for future studies in fully automated vascular interventional surgeries.

Artery and vein diameter ratio measurement based on improvement of arteries and veins segmentation on retinal images.

Retinal arteriolar narrowing is decided based on the artery and vein diameter ratio (AVR). Previous methods segmented blood vessels and classified arteries and veins by color pixels in the centerlines of blood vessels. AVR was definitively determined through measurement of artery and vein diameters. However, this approach was not sufficient for cases with close contact between the artery of interest and an imposing vein. Here, an algorithm for AVR measurement via new classification of arteries and veins is proposed. In this algorithm, additional steps for an accurate segmentation of arteries and veins, which were not identified using the previous method, have been added to better identify major veins in the red channel of a color image. To identify major arteries, a decision tree with three features was used. As a result, all major veins and 90.9% of major arteries were correctly identified, and the absolute mean error in AVRs was 0.12. The proposed method will require further testing with a greater number of images of arteriolar narrowing before clinical application.

Analysis of Fundus Fluorescein Angiogram Based on the Hessian Matrix of Directional Curvelet Sub-bands and Distance Regularized Level Set Evolution.

This paper presents a new procedure for automatic extraction of the blood vessels and optic disk (OD) in fundus fluorescein angiogram (FFA). In order to extract blood vessel centerlines, the algorithm of vessel extraction starts with the analysis of directional images resulting from sub-bands of fast discrete curvelet transform (FDCT) in the similar directions and different scales. For this purpose, each directional image is processed by using information of the first order derivative and eigenvalues obtained from the Hessian matrix. The final vessel segmentation is obtained using a simple region growing algorithm iteratively, which merges centerline images with the contents of images resulting from modified top-hat transform followed by bit plane slicing. After extracting blood vessels from FFA image, candidates regions for OD are enhanced by removing blood vessels from the FFA image, using multi-structure elements morphology, and modification of FDCT coefficients. Then, canny edge detector and Hough transform are applied to the reconstructed image to extract the boundary of candidate regions. At the next step, the information of the main arc of the retinal vessels surrounding the OD region is used to extract the actual location of the OD. Finally, the OD boundary is detected by applying distance regularized level set evolution. The proposed method was tested on the FFA images from angiography unit of Isfahan Feiz Hospital, containing 70 FFA images from different diabetic retinopathy stages. The experimental results show the accuracy more than 93% for vessel segmentation and more than 87% for OD boundary extraction.

Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis.

The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. In this paper we present a novel, semi-automatic method for blood vessel segmentation and centerline extraction, by tracking the blood vessel tree from a user-initiated seed point to the ends of the blood vessel tree. The novelty of our method is in performing only two-dimensional cross-section analysis for segmentation of the connected blood vessels. The cross-section analysis is done by our novel single-scale or multi-scale circle enhancement filter, used at the blood vessel trunk or bifurcation, respectively. The method was validated for both synthetic and medical images. Our validation has shown that the cross-sectional centerline error for our method is below 0.8 pixels and the Dice coefficient for our segmentation is 80%±2.7%. On combining our method with an optional active contour post-processing, the Dice coefficient for the resulting segmentation is found to be 94%±2.4%. Furthermore, by restricting the image analysis to the regions of interest and converting most of the three-dimensional calculations to two-dimensional calculations, the processing was found to be more than 18 times faster than Frangi vesselness with thinning, 8 times faster than user-initiated active contour segmentation with thinning and 7 times faster than our previous method.

Automated computational framework of blood vessel quantification in chick chorioallantoic membrane angiogenesis.

Chick chorioallantoic membrane (CAM) angiogenesis assay has been widely used for finding drugs targeting new blood vessel development in cancer research. In addition to the setup materials and protocols, laboratory findings depend on the quantification and analysis of microscopic blood vessel images. However, it is still a challenging problem because of the high complexity of blood vessel branching structures. We applied preprocessing on CAM microscopic images by keeping the integrity of minor branches in the vessel structure. We then proposed an efficient way to automatically extract blood vessel centerlines based on vector tracing starting from detected seed points. Finally, all branches were coded to construct an abstract model of the branching structure, which enabled more accurate modeling for in-depth analysis. The framework was applied in quantifying Icaritin (ICT) inhibition effects on angiogenesis in a CAM model. Experimental results showed the high accuracy in blood vessel quantification and modeling compared with semimanual measurements. Meanwhile, a set of blood vessel growth indicators were extracted to provide fully automated analysis for angiogenesis assays. Further analysis proved that ICT took effect in a dose-dependent manner which could be applied in suppressing tumor blood vessel growth.

A Novel Retinal Blood Vessel Segmentation Algorithm using Fuzzy segmentation

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose an automatic blood vessel segmentation method. The proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the binary images resulting from centerline detection part with the image resulting from fuzzy vessel segmentation part. In this proposed algorithm, the blood vessel is enhanced using modified morphological operations and the salt and pepper noises are removed from retinal images using Adaptive Fuzzy Switching Median filter. This method is applied on two publicly available databases, the DRIVE and the STARE and the experimental results obtained by using green channel images have been presented and compared with recently published methods. The results demonstrate that our algorithm is very effective method to detect retinal blood vessels. DOI: http://dx.doi.org/10.11591/ijece.v4i4.6253

Two Novel Retinal Blood Vessel Segmentation Algorithms

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose two automatic blood vessel segmentation methods. The first proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the contents of several binary images resulting from vessel width dependent modified morphological filters on normalized retinal images. In the second proposed algorithm the blood vessel is segmented using normalized modified morphological operations and neuro fuzzy classifier. Normalized morphological operations are used to enhance the vessels and neuro fuzzy classifier is used to segment retinal blood vessels. These methods are applied on the publicly available DRIVE database and the experimental results obtained by using green channel images have been presented and their results are compared with recently published methods. The results demonstrate that our algorithms are very effective methods to detect retinal blood vessels. DOI: http://dx.doi.org/10.11591/ijece.v4i3.5829