Automatic detection of coronary stenosis in X-ray angiography through spatio-temporal tracking

Compas, Colin B.; Syeda-Mahmood, Tanveer; McNeillie, Patrick; Beymer, David

doi:10.1109/isbi.2014.6868115

Cited by 17 publications

(17 citation statements)

References 8 publications

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“…These measurements include computing the vessel segment length [1], detecting stenosis [2] and estimating the severity of a stenosis by measuring the vessel's diameter [3]. Segmenting and tracking the artery along the moving angiography sequence is a key step for measuring the artery and assessing its dynamics in the cardiac cycle.…”

Section: Introductionmentioning

confidence: 99%

A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences

M’hiri

Duong

Desrosiers

et al. 2016

Computers in Biology and Medicine

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The segmentation and tracking of coronary arteries (CAs) are critical steps for the computation of biophysical measurements in pediatric interventional cardiology. In the literature, most methods are focused on either segmenting the vessel lumen or on tracking the vessel centerline. However, they do not simultaneously combine the segmentation and tracking of a specific CA. This paper introduces a novel algorithm for CA segmentation and tracking from 2D X-ray angiography sequences. The proposed algorithm is based on the Temporal Vessel Walker (TVW) segmentation method, which combines graph-based formulation and temporal priors. Moreover, superpixel groups are used by TVW as image primitives to ensure a better extraction of the CA. The proposed algorithm, TVW with superpixels (SP-TVW), returns an accurate result to segment and track the artery along the angiogram. Quantitative results over 12 sequences of young patients show the accuracy of the proposed framework. The results return a mean recall of 84% in the dataset. In addition, the proposed method returned a Dice index of 70% in segmenting and tracking right coronary arteries and circumflex arteries. The performance of the proposed method surpasses the existing polyline method in tracking the centerline of CA with a more precise localization of the centerline, resulting in a smaller distance error of 0.23mm compared to 0.94mm.

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Section: Introductionmentioning

confidence: 99%

A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences

M’hiri

Duong

Desrosiers

et al. 2016

Computers in Biology and Medicine

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“…4 and 7 show how, starting from a specified ROI, the method extracts and tracks accurately the section of the vessel. Methods like that proposed by Compas et al [7] track and compute diameter changes in a coronary artery tree to detect stenosis in the vessels but do not show diameter variations at a specific location of the tree.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed approach is a modified version of an existing one [7] to compute a vessel's diameter. After extracting the part of the vessel in the moving sequence, the mean diameter at each frame is measured.…”

Section: Measuring a Vessel's Diametermentioning

confidence: 99%

“…Existing solutions track all the vessels in the sequence, such as the entire coronary tree. This can be useful to detect the presence of stenosis [7] but not to evaluate the elasticity of a specific segment of an artery. In this study we focused on tracking and evaluating a segment of the artery within a specific region of interest (ROI) as determined by a cardiologist.…”

Section: Introductionmentioning

confidence: 99%

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Automatic evaluation of vessel diameter variation from 2D X-ray angiography

et al. 2017

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“…The limitation of this method is the long examination time. [5] Colin B. Compas et. al., exploit the spatiotemporal nature of the X-ray angiography images for creation of coronary vessel tree from the angiogram.…”

Section: Literature Surveymentioning

confidence: 99%

Stenosis Detection Algorithm for Coronary Angiograms

Sambath¹,

Aravindhar²,

Nayar³

2019

IJITEE

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Automatic detection of blocks in the angiographic images is a challenging task. The features such as contrast and gradient of the vessels and the background image are playing a vital role in the detection of the blocks in the X-Ray angiograms. Nowadays, doctors manually identify blocks in the coronary vessels. The automation tool is necessary to identify the blocks in the blood vessels of the heart to help the doctors in the diagnosing process. Spatiotemporal nature of the angiography sequences is used to isolate the coronary artery tree. The coronary artery segment is tracked and in each image frame by frame and the arterial width surface is detected. The stenosis identification is done by using coronary vessel surface’s persistent minima and blob analysis. The proposed method is experimented on 42 patients’ dataset. The performance of the proposed method was evaluated by comparing the blocks identified by the algorithm with the hand-labelled ground truth images given by the experts. The proposed method provides an accuracy of 95.5% on 42 patients with a total of 60 image runs.

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Automatic detection of coronary stenosis in X-ray angiography through spatio-temporal tracking

Cited by 17 publications

References 8 publications

A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences

A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences

Automatic evaluation of vessel diameter variation from 2D X-ray angiography

Stenosis Detection Algorithm for Coronary Angiograms

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