Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography

Sheng, Xiaodong; Fan, Tao; Jin, Xiaoqi; Jin, Jing; Chen, Zhixian; Zheng, Guanqun; Lü, Min; Zhu, Zongcheng

doi:10.1109/access.2019.2955710

Cited by 5 publications

(7 citation statements)

References 29 publications

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“…The goal of our study was to formulate a new method for vessel monitoring and to demonstrate the validity of the proposed method by experiments using simple flow phantoms. We propose this noninvasive method instead of conventional vessel monitoring, which requires expert analysis [3][4][5][6][7][8][9][10][11] or invasive methods [16][17][18][19]. The movement of the vessels cause pressure variations that are transmitted to the skin to produce subtle changes.…”

Section: Discussionmentioning

confidence: 99%

“…Conventional methods for monitoring blood flow include Doppler flowmetry [3,4], spectroscopy [5,6], sonography [7,8], computed tomography [9], electrical capacitance [10], photon attenuation [11], and ultrasound [12], which are used to visualize or analyze vessel structures as well as fluid flow. These methods are commonly used in hospitals as the instruments are expensive, have complicated operations, and require trained operators for accurate analyses [13].…”

Section: Introductionmentioning

confidence: 99%

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Noninvasive Flow Monitoring in Simple Flow Phantom Using Resistive Strain Sensors

Jung

Kim

2021

Sensors

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In this paper, we introduce a monitoring method for flow expansion and contraction in a simple flow phantom based on electrical resistance changes in an epidermal strain sensor attached to the phantom. The flow phantom was fabricated to have a nonflat surface and small modulus that are analogous to human skin. The epidermal sensors made of polydopamine and polyvinyl alcohol show sufficient linearity (R = 0.9969), reproducibility, and self-adhesion properties, as well as high sensitivity to small modulus measurements (<1% tensile strain). Pulsatile flow monitoring experiments were performed by placing the epidermal sensor on the flow phantom and measuring the relative changes in resistance by the heartbeat. Experiments were conducted for three types of vessel diameters (1.5, 2, and 3 mm). In each of the experiments, the vessels were divided into Top, Middle, and Bottom positions. Experiments for each position show that the relative changes in resistance increase proportionally with the diameter of the vessel. The vessels located close to the epidermal layer have greater relative electrical changes. The results were analyzed using the Bernoulli equation and hoop stress formula. This study demonstrates the feasibility of a noninvasive flow monitoring method using a novel resistive strain sensor.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noninvasive Flow Monitoring in Simple Flow Phantom Using Resistive Strain Sensors

Jung

Kim

2021

Sensors

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“…Using deconvolution layers instead of upsampling layers in the proposed U-Net architecture may improve the pixel-wise accuracy of the segmentation. The results can be perfected by incorporating a wider training set with data from multiple benchmarks in the same way as in [14,16,17].…”

Section: Discussionmentioning

confidence: 99%

“…A fully automated centerline extraction was proposed in [14]. The pipeline proposed includes preprocessing by multiscale vessel enhancement filtering and then using a tracking algorithm based on an automatic acquisition of an initial point and direction, combined with forward and backward ridge positioning.…”

Section: Rule-based Centerline Extractionmentioning

confidence: 99%

Coronary Centerline Extraction from CCTA Using 3D-UNet

2021

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The mesh-type coronary model, obtained from three-dimensional reconstruction using the sequence of images produced by computed tomography (CT), can be used to obtain useful diagnostic information, such as extracting the projection of the lumen (planar development along an artery). In this paper, we have focused on automated coronary centerline extraction from cardiac computed tomography angiography (CCTA) proposing a 3D version of U-Net architecture, trained with a novel loss function and with augmented patches. We have obtained promising results for accuracy (between 90–95%) and overlap (between 90–94%) with various network training configurations on the data from the Rotterdam Coronary Artery Centerline Extraction benchmark. We have also demonstrated the ability of the proposed network to learn despite the huge class imbalance and sparse annotation present in the training data.

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“…Coronary artery disease was detected by extracting the blood vessel centerline using CT coronary angiography images in Sheng et al (2019) . First, the coronary artery was refined, resulting in a rough centerline and eliminating wrong branches, loops, and gaps.…”

Section: Related Studiesmentioning

confidence: 99%

Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images

Algarni

Al-Rezqi

Saeed

et al. 2022

PeerJ Computer Science

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Background The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels. Methods In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes. Results The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve. Discussion The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.

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Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography

Cited by 5 publications

References 29 publications

Noninvasive Flow Monitoring in Simple Flow Phantom Using Resistive Strain Sensors

Noninvasive Flow Monitoring in Simple Flow Phantom Using Resistive Strain Sensors

Coronary Centerline Extraction from CCTA Using 3D-UNet

Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images

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