Using angio image sequence for coronary vessel segmentation

Robust implementation of foreground extraction and vessel segmentation for X-ray coronary angiography image sequence

Precise segmentation of continuous vessels in X-ray coronary angiography (XCA) image sequences is pivotal for improving the diagnosis and treatment of coronary artery disease. However, motion artifacts and shadowing in XCA images significantly complicate the segmentation of accurate vessel segmentation. To address these challenges, we propose FlowVM-Net, a dynamic information-enhanced encoder-decoder architecture. The model incorporates an optical flow generation module to create temporal information across image sequences. Additionally, we introduce a wavelet dilated convolution visual state space model block based on VMamba as a fundamental component of the encoder-decoder structure. An attention-based optical flow feature fusion module is designed to effectively integrate sequential spatial features and temporal information. Furthermore, a composite loss function, including boundary difference over union loss, is employed to enhance the accuracy of vessel edge and thin vessel segmentation. We evaluate FlowVM-Net on a dataset of 542 samples, achieving a DSC of 85.17%, a sensitivity of 85.15%, and a quality score of 90.49% on the test set. The proposed network effectively preserves vessel continuity, accurately segments thin vessels, and demonstrates the potential of leveraging dynamic context from XCA sequences for improved coronary artery segmentation. The code for this project is available at: https://github.com/wgyhhhh/FlowVM-Net .

Heart disease is the leading cause of death worldwide, responsible for 16% of the world’s total deaths. Coronary artery disease is the most common type of heart disease and is caused by plaque buildup in the wall of the arteries. X-ray coronary angiography is the gold standard for assessing coronary artery disease via estimation of the percentage of narrowing, also known as stenosis. However, errors in physicians’ interpretation of stenosis severity could lead to overuse and underuse of revascularization. Automated techniques can help clinicians in their decision-making. In this paper, we review state-of-the-art techniques developed for coronary artery segmentation, vessel modeling, and stenosis detection. The methods are categorized using criteria such as segmentation methods, evaluation metrics, and validation approaches.

Vessel segmentation is critical for disease diagnosis and surgical planning. Recently, the vessel segmentation method based on deep learning has achieved outstanding performance. However, vessel segmentation remains challenging due to thin vessels with low contrast that easily lose spatial information in the traditional U-shaped segmentation network. To alleviate this problem, we propose a novel and straightforward full-resolution network (FR-UNet) that expands horizontally and vertically through a multiresolution convolution interactive mechanism while retaining full image resolution. In FR-UNet, the feature aggregation module integrates multiscale feature maps from adjacent stages to supplement high-level contextual information. The modified residual blocks continuously learn multiresolution representations to obtain a pixel-level accuracy prediction map. Moreover, we propose the dual-threshold iterative algorithm (DTI) to extract weak vessel pixels for improving vessel connectivity. The proposed method was evaluated on retinal vessel datasets (DRIVE, CHASE_DB1, and STARE) and coronary angiography datasets (DCA1 and CHUAC). The results demonstrate that FR-UNet outperforms state-of-the-art methods by achieving the highest Sen, AUC, F1, and IOU on most of the above-mentioned datasets with fewer parameters, and that DTI enhances vessel connectivity while greatly improving sensitivity. The code is available at: https://github.com/lseventeen/FR-UNet.

Coronary heart disease has been one of the main threats to human health. Coronary angiography is taken as the gold standard; for the assessment of coronary artery disease. However, sometimes, the images are difficult to visually interpret because of the crossing and overlapping of vessels in the angiogram. Vessel extraction from X-ray angiograms has been a challenging problem for several years. There are several problems in the extraction of vessels, including: weak contrast between the coronary arteries and the background, unknown and easily deformable shape of the vessel tree, and strong overlapping shadows of the bones. In this article we investigate the coronary vessel extraction and enhancement techniques, and present capabilities of the most important algorithms concerning coronary vessel segmentation.

Background: An organizational report indicates that heart attacks lead to seventy percent of human fatalities. Heart-related diseases strike people in India who range between the ages of 30-60 years. X-ray coronary angiography functions as the key procedure for detecting these conditions. The manual process of heart vessel segmentation by cardiologists becomes slow and needs significant effort because different professional skill levels affect the consistency of their output results. Objective: A study proposes automatic coronary angiography segmentation through artificial intelligence analysis of edge features to accurately detect the main cardiovascular artery system edges. Materials and methods: The Mendeley public database contained 100 patient images for training purposes and 34 images for validation purposes. The VGG Image Annotator tool served to create binary masks for annotation purposes. The analysis incorporated traditional edge detection methods that included Sobel, Prewitt, and Roberts along with Canny. Results: The tested model obtained 99% accuracy alongside a positive predictive value (PPV) of 96% and Sensitivity of 94% and Dice Coefficient of 95%. The upcoming research will focus on developing soft computing approaches for detecting stenosis in segmented images. Conclusion: The method demonstrates better performance metrics that show superior capability to previous techniques implemented in this field. New studies are needed to analyze soft computing techniques to identify vascular structures in coronary angiographic images.

Narrowing of the coronary arteries is an important indicator of the severity of coronary artery disease (CAD) in patients. Previous research using deep learning to identify narrowed vessels has primarily been based on object detection, but this approach has several problems, such as the inability to detect multivessel narrowing, calculate the actual rate of narrowing, and determine the exact location of occurrence. Therefore, we believe that the determination of narrowing should return to the original method, namely, calculating the maximum and minimum diameters around the vessel. However, to automate this task, a preprocessing step for separating each vessel is needed, especially for the complex structure of the left coronary artery. We designed a deep learning model capable of analyzing an entire coronary angiography (CAG) image for the separation and tracking of vessels. This not only serves as a preprocessing step for calculating narrowing but also allows for the analysis of different vessel flow directions, providing doctors with comprehensive assessment indicators.

For computer-aided diagnosis of cardiovascular diseases, accurately extracted centerlines of coronary arteries in computed tomography (CT) angiographic images are important, because they are the basis of curved multi-planar reformation (cMPR) creation and stenosis analysis. Within all the centerline extraction methods, the methods performed after vessel segmentation are easier to implement, of less running time and more robust, and for these reasons, they are still used in many practical applications. However, because of segmentation errors and vascular abnormalities, segmented vessels are not always correct. If centerline extraction seriously depends on vessel segmentation, incorrectly segmented vessels may probably result in errors in their centerlines (like incorrect local curvatures of centerlines). To obtain correct centerlines, we need to identify the incorrectly segmented vessels first, and then correct the centerlines of these vessels. In this paper, we propose two automatic methods to identify the incorrectly segmented vessels and correct their centerlines respectively. For the identification of incorrect vessel segmentation, we propose a method based on the vessel diameter fitting. And for centerline correction, we propose a method based on the precise smoothness to draw deviated centerlines back to the correct locations. We have validated the proposed methods on real CT angiographic datasets of coronary arteries. The quantitative evaluation results show that the proposed methods can effectively detect and correct centerline errors arising from the erroneous vessel segmentation in most cases. And we can see from the experimental results that the number of false positive and false negative center points reduces a lot when compared with the original results without centerline correction.

Automatic detection of initial seed points has become an essential step towards delineating coronary arteries in coronary computed tomography angiography (CCTA) images due to image inhomogeneity and other factors. Most coronary segmentation algorithms require user interaction for seed point selection, which may lead to erroneous segmentation. In this study, we present an improved technique of seed detection for coronary segmentation using a thresholded Frangi response. Before computing region of interest (ROI), the proposed method first computes the Frangi response of the complete CCTA volume, followed by thresholding with respect to quantile and median values, and then the ROI selection procedure is applied. Further, this procedure is joined with a feature that is built according to the resemblance among consecutive orthogonal cross-sections. The proposed method was evaluated on nine clinical datasets, and the proposed framework automatically detected coronary seeds accurately and can be used for an accurate delineation of coronary arteries. The obtained results were compared qualitatively and quantitatively by a radiologist, and the proposed method outperformed the previous method with an improvement of 45.9%.

IntroductionHeart rate irregularities are the major limitations of computed tomographic coronary angiography (CTCA) due to severe motion artifacts.AimTo evaluate the safety and efficacy of a lidocaine intravenous bolus in preserving good image quality by the transient suppression of premature ventricular contractions (PVC) during the CTCA scan.Material and methodsThe study group comprised 67 consecutive patients with sinus rhythm and numerous PVC scheduled for CTCA. Intravenous boluses of 25–50 mg lidocaine were given after calcium score assessment and immediately before CTCA. The control group comprised 67 patients with sinus rhythm without PVC matched according to the body mass index (BMI), age, sex, and calcium score. All coronary vessel segments were assessed for image quality and presence of significant stenosis.ResultsAs compared with calcium score assessment, after administration of lidocaine and during the CTCA scan PVC were completely suppressed in 22 (40%), reduced in 10 (18%), unchanged in 18 (32%), and intensified in 5 (10%) patients. Overall, there were 32 (58%) patients with sinus rhythm during CTCA as compared with only 11 (20%) patients free from PVC during calcium score assessment (p < 0.001). Image quality in 871 coronary segments including both the study group and control patients was worse in patients with PVC (p < 0.0001). However, there was no statistically significant difference in the number of patients with at least one segment of non-diagnostic quality (6% vs. 12%, p = 0.36; respectively).ConclusionsSingle lidocaine bolus given prior to CTCA is safe, may temporarily eliminate or reduce the intensity of arrhythmia, and hence results in improved quality of CTCA in patients with numerous PVC.

A realistic 3D coronary arterial tree (CAT) has been developed for the heart model of the computer generated 3D XCAT phantom. The CAT allows generation of a realistic model of the location, size and shape of the associated regional ischemia or infarction for a given coronary arterial stenosis or occlusion. This in turn can be used in medical imaging applications. An iterative rule-based generation method that systematically utilized anatomic, morphometric and physiologic knowledge was used to construct a detailed realistic 3D model of the CAT in the XCAT phantom. The anatomic details of the myocardial surfaces and large coronary arterial vessel segments were first extracted from cardiac CT images of a normal patient with right coronary dominance. Morphometric information derived from porcine data from the literature, after being adjusted by scaling laws, provided statistically nominal diameters, lengths, and connectivity probabilities of the generated coronary arterial segments in modeling the CAT of an average human. The largest six orders of the CAT were generated based on the physiologic constraints defined in the coronary generation algorithms. When combined with the heart model of the XCAT phantom, the realistic CAT provides a unique simulation tool for the generation of realistic regional myocardial ischemia and infraction. Together with the existing heart model, the new CAT provides an important improvement over the current 3D XCAT phantom in providing a more realistic model of the normal heart and the potential to simulate myocardial diseases in evaluation of medical imaging instrumentation, image reconstruction, and data processing methods.

In this paper we present an automated method for segmentation of coronary vessels in X-ray angiogram images. Firstly the angiograms are registered to omit the artifacts as much as possible. Secondly geometric active contours are used as a powerful tool of segmentation. To facilitate convergence of active contours, the centerlines of the vessels are detected by a tracking method. By this procedure even very thin and low contrast vessels are also detected successfully. The results show the superiority of proposed algorithm over those in literature.

"This paper presents a new coronary artery segmentation method in X-ray angiographic images consisting of two stages. In the first stage, a multiscale top-hat operator based on the properties of the Hessian matrix is introduced to enhance vessel-like structures in the angiogram. The results of the proposed multiscale top-hat operator are compared with multiscale methods based on Gaussian matched filters, Hessian matrix and morphological operators, and analyzed using the area (zA) under the receiver operating characteristic curve. In the second stage, a new thresholding method based on multiobjective optimization following the weighted sum approach to classify vessel and nonvessel pixels is presented. The performance of the multiobjective method is compared with seven automatic thresholding methods using the ground-truth angiograms drawn by a specialist with the sensitivity, specificity and accuracy measures. Finally, the proposed method is compared with five state-of-the-art vessel segmentation methods. The vessel enhancement results using the multiscale top-hat operator demonstrated the highest accuracy with zA = 0.942 with a training set of 40 angiograms and zA= 0.965 with a test set of 40 angiograms. The results of coronary artery segmentation using the multiobjective thresholding method provided an average accuracy performance of 0.923 with the test set of angiograms."

Multiscale Graph Cuts Based Method for Coronary Artery Segmentation in Angiograms

Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in X-ray images