Vessel Tracking Algorithm Research Articles

Lightweight multiobject ship tracking algorithm based on trajectory association and improved YOLOv7tiny

In response to various challenges, such as high computational complexity, large parameter counts, and frequent vessel ID switching in ship tracking models, we propose a trajectory association and improved YOLOv7tiny-based vessel tracking algorithm, named the vessel tracking model with YOLOv7tiny-Vessel and StrongSORT-Vessel (VTM-YS). The algorithm consists of two stages: vessel detection and vessel tracking. In the vessel detection stage, we propose a lightweight Fast-PP-LCNet backbone network for efficient feature extraction of ships. Then, a novel Slim Bidirectional Pyramid Aggregation Network (Slim-BiPANet) is constructed to effectively fuse multiscale vessel features. Furthermore, introducing a coordinate attention (CA) module enhances the focus on vessel detection regions. In the vessel tracking stage, we introduce the OSNet reidentification network and the NSA Kalman filter algorithm into the StrongSORT model to reduce the frequency of tracked vessel ID switches. The results indicate that, in terms of vessel detection, the improved YOLOv7tiny achieves a 1.5% increase in mAP50 and a 13.9% reduction in parameters compared to the baseline YOLOv7tiny. For vessel tracking, the StrongSORT model achieves a 3.5% improvement in HOTA, a 7.0% improvement in MOTA, and a 73% reduction in ID switches, meeting the requirements for lightweight design and tracking accuracy.

Learning-based algorithms for vessel tracking: A review.

Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.

Near infrared oximetry-guided artery-vein classification in optical coherence tomography angiography.

Differential artery–vein analysis is valuable for early detection of diabetic retinopathy and other eye diseases. As a new optical coherence tomography imaging modality, optical coherence tomography angiography provides capillary level resolution for accurate examination of retinal vasculatures. However, differential artery–vein analysis in optical coherence tomography angiography particularly for macular region in which blood vessels are small is challenging. In coordination with an automatic vessel tracking algorithm, we report here the feasibility of using near infrared optical coherence tomography oximetry to guide artery–vein classification in optical coherence tomography angiography of macular region. Impact statement It is known that arteries and veins can be affected by retinal diseases differently. Therefore, quantitative artery–vein analysis holds the promise for better disease detection and treatment evaluation. However, clinical optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) instruments lack the function of differential artery–vein analysis. Here, we report the feasibility of near infrared OCT oximetry-guided artery–vein classification in OCTA. Because the OCT and OCTA are naturally captured from the same instrument simultaneously, the presented method is feasible for practical deployment of differential artery–vein analysis in OCTA.

OCT feature analysis guided artery-vein differentiation in OCTA.

Differential artery-vein analysis promises better sensitivity for retinal disease detection and classification. However, clinical optical coherence tomography angiography (OCTA) instruments lack the function of artery-vein differentiation. This study aims to verify the feasibility of using OCT intensity feature analysis to guide artery-vein differentiation in OCTA. Four OCT intensity profile features, including i) ratio of vessel width to central reflex, ii) average of maximum profile brightness, iii) average of median profile intensity, and iv) optical density of vessel boundary intensity compared to background intensity, are used to classify artery-vein source nodes in OCT. A blood vessel tracking algorithm is then employed to automatically generate the OCT artery-vein map. Given the fact that OCT and OCTA are intrinsically reconstructed from the same raw spectrogram, the OCT artery-vein map is able to guide artery-vein differentiation in OCTA directly.

Combining ODR and Blood Vessel Tracking for Artery-Vein Classification and Analysis in Color Fundus Images.

PurposeThis study aims to develop a fully automated algorithm for artery–vein (A-V) and arteriole-venule classification and to quantify the effect of hypertension on A-V caliber and tortuosity ratios of nonproliferative diabetic retinopathy (NPDR) patients.MethodsWe combine an optical density ratio (ODR) analysis and blood vessel tracking (BVT) algorithm to classify arteries and veins and arterioles and venules. An enhanced blood vessel map and ODR analysis are used to determine the blood vessel source nodes. The whole vessel map is then tracked beginning from the source nodes and classified as vein (venule) or artery (arteriole) using vessel curvature and angle information. Fifty color fundus images from NPDR patients are used to test the algorithm. Sensitivity, specificity, and accuracy metrics are measured to validate the classification method compared to ground truths.ResultsThe combined ODR-BVT method demonstrates 97.06% accuracy in identifying blood vessels as vein or artery. Sensitivity and specificity of A-V identification are 97.58%, 97.81%, and 95.89%, 96.68%, respectively. Comparative analysis revealed that the average A-V caliber and tortuosity ratios of NPDR patients with hypertension have 48% and 15.5% decreases, respectively, compared to that of NPDR patients without hypertension.ConclusionsAutomated A-V classification has been achieved by combined ODR-BVT analysis. Quantitative analysis of color fundus images verified robust performance of the A-V classification. Comparative quantification of A-V caliber and tortuosity ratios provided objective biomarkers to differentiate NPDR groups with and without hypertension.Translational RelevanceAutomated A-V classification can facilitate quantitative analysis of retinal vascular distortions due to diabetic retinopathy and other eye conditions and provide increased sensitivity for early detection of eye diseases.

Automated classification and quantitative analysis of arterial and venous vessels in fundus images.

It is known that retinopathies may affect arteries and veins differently. Therefore, reliable differentiation of arteries and veins is essential for computer-aided analysis of fundus images. The purpose of this study is to validate one automated method for robust classification of arteries and veins (A-V) in digital fundus images. We combine optical density ratio (ODR) analysis and blood vessel tracking algorithm to classify arteries and veins. A matched filtering method is used to enhance retinal blood vessels. Bottom hat filtering and global thresholding are used to segment the vessel and skeleton individual blood vessels. The vessel tracking algorithm is used to locate the optic disk and to identify source nodes of blood vessels in optic disk area. Each node can be identified as vein or artery using ODR information. Using the source nodes as starting point, the whole vessel trace is then tracked and classified as vein or artery using vessel curvature and angle information. 50 color fundus images from diabetic retinopathy patients were used to test the algorithm. Sensitivity, specificity, and accuracy metrics were measured to assess the validity of the proposed classification method compared to ground truths created by two independent observers. The algorithm demonstrated 97.52% accuracy in identifying blood vessels as vein or artery. A quantitative analysis upon A-V classification showed that average A-V ratio of width for NPDR subjects with hypertension decreased significantly (43.13%).

지역적 다수의 경로를 이용한 혈관 추적 알고리즘

지역적 다수의 경로를 이용한 혈관 추적 알고리즘

Automatic detection of aorto-femoral vessel trajectory from whole-body computed tomography angiography data sets.

Extraction of the aorto-femoral vessel trajectory is important to utilize computed tomography angiography (CTA) in an integrated workflow of the image-guided work-up prior to trans-catheter aortic valve replacement (TAVR). The aim of this study was to develop a new, fully-automated technique for the extraction of the entire arterial access route from the femoral artery to the aortic root. An automatic vessel tracking algorithm was first used to find the centerline that connected the femoral accessing points and the aortic root. Subsequently, a deformable 3D-model fitting method was used to delineate the lumen boundary of the vascular trajectory in the whole-body CTA dataset. A validation was carried out by comparing the automatically obtained results with semi-automatically obtained results from two experienced observers. The whole framework was validated on whole body CTA datasets of 36 patients. The average Dice similarity indexes between the segmentations of the automatic method and observer 1 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta were 0.977 ± 0.030, 0.980 ± 0.019, 0.982 ± 0.016; the average Dice similarity indexes between the segmentations of the automatic method and observer 2 were 0.950 ± 0.040, 0.954 ± 0.031 and 0.965 ± 0.019, respectively. The inter-observer variability resulted in a Dice similarity index of 0.954 ± 0.038, 0.952 ± 0.031 and 0.969 ± 0.018 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta. The average minimal luminal diameters (MLDs) of the ilio-femoral artery were 6.03 ± 1.48, 5.70 ± 1.43 and 5.52 ± 1.32mm for the automatic method, observer 1 and observer 2 respectively. The MLDs of the aorta were 13.43 ± 2.54, 12.40 ± 2.93 and 12.08 ± 2.40mm for the automatic method, observer 1 and observer 2 respectively. The automatic measurement overestimated the MLD slightly in the ilio-femoral artery at the average by 0.323mm (SD = 0.49mm, p < 0.001) compared to observer 1 and by 0.51mm (SD = 0.71mm, p < 0.001) compared to observer 2. The proposed segmentation approach can automatically provide reliable measurements of the entire arterial accessing route that can be used to support TAVR procedures. To the best of our knowledges, this approach is the first fully automatic segmentation method of the whole aorto-femoral vessel trajectory in CTA images.

Unscented Kalman Snake for 3D Vessel Tracking

Purpose In this paper, we propose a robust 3D vessel tracking algorithm by utilizing an active contour model and unscented Kalman filter which are the two representative algorithms on segmentation and tracking. Materials and Methods The proposed algorithm firstly accepts user input to produce an initial estimate of vessel boundary segmentation. On each Computed Tomography Angiography (CTA) slice, the active contour is applied to segment the vessel boundary. After that, the estimation process of the unscented Kalman filter is applied to track the vessel boundary of the current slice to estimate the inter-slice vessel position translation and shape deformation. Finally both active contour and unscented Kalman filter are inter-operated for vessel segmentation of the next slice. Results The arbitrarily shaped blood vessel boundary on each slice is segmented by using the active contour model, and the Kalman filter is employed to track the translation and shape deformation between CTA slices. The proposed algorithm is applied to the 3D visualization of chest CTA images using graphics hardware. Conclusion Through this algorithm, more opportunities, giving quick and brief diagnosis, could be provided for the radiologist before detailed diagnosis using 2D CTA slices, Also, for the surgeon, the algorithm could be used for surgical planning, simulation, navigation and rehearsal, and is expected to be applied to highly valuable applications for more accurate 3D vessel tracking and rendering.

Automatic estimation of the arteriolar‐to‐venular diameter ratio (AVR) in retinal images

Abstract Purpose The Arteriolar‐to‐Venular diameter Ratio (AVR), a parameter derived from vessel caliber measurements in a specific region of retinal images, is used as a descriptor of generalized arteriolar narrowing. We developed a computerized system to compute AVR in a totally automatic way. Methods Images are at first enhanced to highlight the vessel network, which is then traced by a vessel tracking algorithm. From the detected vessel structure, the position of the optic disc is derived and the region inside which the AVR data are to be measured is determined. Vessels within this region are classified as either arteries or veins, their caliber estimated and the AVR parameter is eventually computed. Results provided by the system have been compared with manually derived AVR values on 20 eye fundus images. Results Mean and SD values from the two sets of measurements are the same, and automatic/manual ratios have an average value of 1 and 95% confidence interval of (0.98‐1.02). The correlation coefficient between the two methods is 0.88. In only two images the ratio is appreciably different from unity, 1.14 and 0.89 respectively. A detailed analysis of these cases revealed that in each image the wrong classification of one vessel only was the cause for these unsatisfactory results. When these misclassifications are manually corrected, e.g. with a quick editing tool that can be easily made available to the user, the ratios become 0.95 and 0.99, respectively, and the overall correlation coefficient becomes 0.97. Conclusion Additional evaluation on a larger set of images, acquired from subjects exhibiting wide variations of AVR, will be performed in order to fully assess the reliability and clinical applicability of this automatic procedure.

A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering.

In this paper we present a new unsupervised fuzzy algorithm for vessel tracking that is applied to the detection of the ocular fundus vessels. The proposed method overcomes the problems of initialization and vessel profile modeling that are encountered in the literature and automatically tracks fundus vessels using linguistic descriptions like "vessel" and "nonvessel." The main tool for determining vessel and nonvessel regions along a vessel profile is the fuzzy C-means clustering algorithm that is fed with properly preprocessed data. Additional procedures for checking the validity of the detected vessels and handling junctions and forks are also presented. The application of the proposed algorithm to fundus images and simulated vessels resulted in very good overall performance and consistent estimation of vessel parameters.

Epicardial surface estimation from coronary angiograms

We describe a system for estimating the epicardial surface of the heart using data obtained from biplane coronary cinéangiograms. The 3-dimensional (3D) geometry of the left coronary arterial tree at an instant in time is interactively reconstructed as an ensemble of 3D Bézier cubics. This provides a compact representation of the tree structure, incorporating the 3D location of the bifurcation points and their connectivity as well as the locii of the connecting vessels. A finite element model for the surface is defined using bicubic Hermite basis functions to interpolate prolate spheroidal geometric parameters. As the arteries are not uniformly distributed around the left ventricular epicardium, weighted spline-type smoothness constraints are incorporated into the error function along with the least squares error estimate. A trial fit to data describing the superficial arteries of an isolated dog heart was compared to a uniform and dense data set covering the entire epicardial surface obtained from the same heart. Good agreement was found in elements containing coronary data, with the error increasing in a controlled manner in the remaining elements. Results with angiographic data are also given. The model is readily extended to fit time-varying surfaces with the inclusion of a time basis function and is intended for use in subsequent vessel tracking algorithms.