3D Reconstruction of Patient-Specific Carotid Artery Geometry Using Clinical Ultrasound Imaging (original) (raw)
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2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), 2020
It is important to investigate the state of the arteries in order to detect atherosclerotic plaques in the early stage and then treat them appropriately. One of the diagnostic techniques is the ultrasound (US) examination. In order to obtain a more detailed and comprehensive overview of the state of the patient’s carotid artery, 3D reconstruction using the available 2D cross-sections can be performed. In this paper, deep learning is used for the automatic segmentation of US images, and this data is then used to reconstruct the 3D model of the patient-specific carotid artery. The validation of the proposed approach is performed by comparing two relevant clinical parameters for accessing the severity of vessel stenosis – the plaque length and the percentage of stenosis. Good validation results demonstrate that this method is capable of accurately performing segmentation of the lumen of carotid artery from US images and thus it can be a useful tool for assessing the state of the arteri...
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020
The scope of this paper is to present a new carotid vessel segmentation algorithm implementing the U-net based convolutional neural network architecture. With carotid atherosclerosis being the major cause of stroke in Europe, new methods that can provide more accurate image segmentation of the carotid arterial tree and plaque tissue can help improve early diagnosis, prevention and treatment of carotid disease. Herein, we present a novel methodology combining the U-net model and morphological active contours in an iterative framework that accurately segments the carotid lumen and outer wall. The method automatically produces a 3D meshed model of the carotid bifurcation and smaller branches, using multispectral MR image series obtained from two clinical centres of the TAXINOMISIS study. As indicated by a validation study, the algorithm succeeds high accuracy (99.1% for lumen area and 92.6% for the perimeter) for lumen segmentation. The proposed algorithm will be used in the TAXINOMISIS study to obtain more accurate 3D vessel models for improved computational fluid dynamics simulations and the development of models of atherosclerotic plaque progression. I.
IEEE Journal of Biomedical and Health Informatics
For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential part in this monitoring process. In this paper, we propose a novel Deep Convolutional Neural Network as a fully automated segmentation tool, and its application in delineating both the media-adventitia boundary and the lumen-intima boundary. We develop a new geometrically constrained objective function as part of the Network's Stochastic Gradient Descent optimisation, thus tuning it to the problem at hand. Furthermore, we also apply a novel, bimodal fusion of envelope and phase congruency data as an input to the network, as the latter provides an intensity-invariant data source to the network. We finally report the segmentation performance of the network on transverse sections of the carotid. Tests are carried out on an augmented dataset of 81,000 images, and the results are compared to other studies by reporting the DICE coefficient of similarity, modified Hausdorff Distance, sensitivity and specificity. Our proposed method is shown to yield results of comparable accuracy over this larger dataset, with the advantage of it being fully automated. We conclude that Deep Convolutional Neural Networks provide a reliable trained manner in which carotid ultrasound images may be automatically segmented, using envelope data and intensity invariant phase congruency maps as a data source.
Reconstruction and quantification of the carotid artery bifurcation from 3-D ultrasound images
IEEE Transactions on Medical Imaging, 2004
Three-dimensional (3-D) ultrasound is a relatively new technique, which is well suited to imaging superficial blood vessels, and potentially provides a useful, noninvasive method for generating anatomically realistic 3-D models of the peripheral vasculature. Such models are essential for accurate simulation of blood flow using computational fluid dynamics (CFD), but may also be used to quantify atherosclerotic plaque more comprehensively than routine clinical methods. In this paper, we present a spline-based method for reconstructing the normal and diseased carotid artery bifurcation from images acquired using a freehand 3-D ultrasound system. The vessel wall (intima-media interface) and lumen surfaces are represented by a geometric model defined using smoothing splines. Using this coupled wall-lumen model, we demonstrate how plaque may be analyzed automatically to provide a comprehensive set of quantitative measures of size and shape, including established clinical measures, such as degree of (diameter) stenosis. The geometric accuracy of 3-D ultrasound reconstruction is assessed using pulsatile phantoms of the carotid bifurcation, and we conclude by demonstrating the in vivo application of the algorithms outlined to 3-D ultrasound scans from a series of patient carotid arteries.
3D Reconstruction of Carotid Artery from Ultrasound Images
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
3D reconstruction is an important area in computer vision, which can be applied to assist in medical diagnosis. Compared to observing 2D ultrasound images, 3D models are more suitable for diagnostic interpretation. In this paper, we describe an approach for 3D reconstruction of the carotid artery utilizing ultrasound images from the transverse and longitudinal views. We implement a human-computer interface to ensure the accuracy of the segmentation results by involving superpixels and ellipse fitting techniques. This approach is expected to achieve better accuracy to assist diagnostics in the future.
Sensors
The aim of this study was to evaluate the feasibility of a noninvasive and low-operator-dependent imaging method for carotid-artery-stenosis diagnosis. A previously developed prototype for 3D ultrasound scans based on a standard ultrasound machine and a pose reading sensor was used for this study. Working in a 3D space and processing data using automatic segmentation lowers operator dependency. Additionally, ultrasound imaging is a noninvasive diagnosis method. Artificial intelligence (AI)-based automatic segmentation of the acquired data was performed for the reconstruction and visualization of the scanned area: the carotid artery wall, the carotid artery circulated lumen, soft plaque, and calcified plaque. A qualitative evaluation was conducted via comparing the US reconstruction results with the CT angiographies of healthy and carotid-artery-disease patients. The overall scores for the automated segmentation using the MultiResUNet model for all segmented classes in our study were...
3D dynamic model of healthy and pathologic arteries for ultrasound technique evaluation
Medical Physics, 2008
A 3D model reproducing the biomechanical behavior of human blood vessels is presented. The model, based on a multilayer geometry composed of right generalized cylinders, enables the representation of different vessel morphologies, including bifurcations, either healthy or affected by stenoses. Using a finite element approach, blood flow is simulated by considering a dynamic displacement of the scatterers ͑erythrocytes͒, while arterial pulsation due to the hydraulic pressure is taken into account through a fluid-structure interaction based on a wall model. Each region is acoustically characterized using FIELD II software, which produces the radio frequency echo signals corresponding to echographic scans. Three acoustic physiological phantoms of carotid arteries surrounded by elastic tissue are presented to illustrate the model's capability. The first corresponds to a healthy blood vessel, the second includes a 50% stenosis, and the third represents a carotid bifurcation. Examples of M mode, B mode and color Doppler images derived from these phantoms are shown. Two examples of M-mode image segmentation and the identification of the atherosclerotic plaque boundaries on Doppler color images are reported. The model could be used as a tool for the preliminary evaluation of ultrasound signal processing and visualization techniques.
Tracking transverse 2D ultrasound images of the carotid artery
2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2014
This paper focuses on tracking transverse 2D ultrasound images as a means to produce a 3D representation of a bifurcating blood vessel in order to provide a foundation for autonomous aneurysm detection and classification, and atherosclerosis/atherosclerotic narrowing detection. In any healthcare system, a means to achieve the goal of automating point of care testing is desirable, especially given that a NHS study shows that 1.5% of men aged 65 studied had aortic diameters of greater than three centimeters [1]. In this paper, a method for creating a 3D representation of the carotid artery, determining and modeling the bifurcation region, and determining a subjects heart rate based on vascular distension in order to provide a foundation for autonomous point of care testing is presented.
3D reconstruction of a carotid bifurcation from 2D transversal ultrasound images
Ultrasonics, 2014
Visualizing and analyzing the morphological structure of carotid bifurcations are important for understanding the etiology of carotid atherosclerosis, which is a major cause of stroke and transient ischemic attack. For delineation of vasculatures in the carotid artery, ultrasound examinations have been widely employed because of a noninvasive procedure without ionizing radiation. However, conventional 2D ultrasound imaging has technical limitations in observing the complicated 3D shapes and asymmetric vasodilation of bifurcations. This study aims to propose image-processing techniques for better 3D reconstruction of a carotid bifurcation in a rat by using 2D cross-sectional ultrasound images. A high-resolution ultrasound imaging system with a probe centered at 40MHz was employed to obtain 2D transversal images. The lumen boundaries in each transverse ultrasound image were detected by using three different techniques; an ellipse-fitting, a correlation mapping to visualize the decorre...
Segmentation of the Carotid Arteries from 3D Ultrasound Images
Advanced Computational Approaches to Biomedical Engineering, 2013
Ultrasound (US) Doppler flow-velocity imaging has been used extensively in the diagnosis and management of carotid atherosclerosis. Doppler ultrasound-based measurement is a well-established screening tool for the assessment of stenosis severity. However, this method of measurement does not provide information on carotid plaque morphology, plaque vulnerability, or composition. In addition to stenosis severity, US-based phenotypes of carotid atherosclerosis include intima-media thickness (IMT), and total plaque area (TPA). More recently, vessel-wall-volume (VWV) and total-plaque-volume (TPV) have emerged as sensitive and useful US phenotypes of carotid atherosclerosis that measure plaque burden in 3D images. In order to accelerate the translation of these 3D US-based carotid atherosclerosis measurements to clinical practice, semiautomated methods of measurement are required to enable multiple observers to be trained in shorter time periods and with decreased inter-observer variability. This has stimulated investigators to develop accurate and robust segmentation algorithms allowing efficient quantification of carotid atherosclerosis. In this chapter, we demonstrate that 3D US is a viable technique for quantifying the progression and regression of carotid atherosclerosis and describe algorithms for segmentation of carotid vessels.