Vessel Walker: Coronary arteries segmentation using random walks and hessian-based vesselness filter

M’hiri, Faten; Duong, Luc; Desrosiers, Christian; Cheriet, Mohamed

doi:10.1109/isbi.2013.6556625

Cited by 37 publications

(26 citation statements)

References 10 publications

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“…At the beginning of the sequence, the clinician defines the vessel of interest by specifying -using a simple click-the limits of the vessel. The vessel walker method (VW) [15] with superpixels is applied to segment all the vessels in the image. This result is further refined to extract only the vessel of interest based on the selected seeds, as described in section 3.2.1.…”

Section: Superpixel Methodsmentioning

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: Superpixel Methodsmentioning

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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“…By differentiating the previous equation with respect to f and by setting γ = β, in a similar manner to [10], we obtain:…”

Section: Volume Reconstructionmentioning

confidence: 99%

3D reconstruction of vascular structures using graph-based voxel coloring

Martin

Vachon

Miró

et al. 2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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Biplane X-ray angiography is currently the gold standard for navigational guidance during percutaneous interventions in vascular structures; but it remains limited to 2D projections. In this study, we propose a novel graph-based voxel coloring method for 3D reconstruction of vascular structures from biplane X-ray angiography sequences. The reconstruction is obtained by using the random walks algorithm on a graphbased representation of a discretized visual hull, to obtain the probability of belonging to the vascular structure. A multiscale scheme is introduced to reconstruct at a finer level, while being computationally efficient. The proposed method was validated using the XCAT motion simulator and on calibrated clinical data.

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“…In the first frame of the 2D X-ray sequence, the section of the vessel is segmented using our Vessel Walker (VW) segmentation method [17]. This method extracts vessels in X-ray images by combining a graph-based representation and vesselness features.…”

Section: Segmentation and Tracking Of A Section Of Arterymentioning

confidence: 99%

“…They are either based on variational approaches such as level sets [28], or on graph-based methods such as graph cuts [23], or random walks [29]. In the current study, we used a method we developed called Vessel Walker [17] that extends the random walks method [10] to segment blood vessels. Second, vessels like coronary arteries are subject to a combination of cardiac and respiratory motions, which are challenging to model, as observed by Shechter et al [24].…”

Section: Introductionmentioning

confidence: 99%