Automatic segmentation of the wire frame of stent grafts from CT data

Klein, Almar; Vliet, J. Adam van der; Oostveen, Luuk J.; Hoogeveen, Yvonne L.; Kool, Leo J. Schultze; Renema, W. Klaas Jan; Slump, Cornelis H.

doi:10.1016/j.media.2011.05.015

Cited by 17 publications

(37 citation statements)

References 53 publications

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“…The stent can consist of CT values as low as 300 HU [65]. There are also reports of some stent types being barely distinguishable, whereas other stent types are well visible in data obtained using the same scanner settings [66].…”

Section: Segmentation Of the Stent Graftmentioning

confidence: 99%

“…It has been shown that combining a subset of all volumes in the sequence can produce better results due to a more optimal compromise between noise and motion blur [66].…”

Section: Combining the Volumesmentioning

confidence: 99%

“…In [66] a geometric model is proposed that represents the wire frame of the stent as an undirected graph, with nodes placed at the corners and crossings of the frame, and the edges between the nodes representing the wires (Figure 2.7). This model can be applied to different stent types, and represents the topology of the stent's frame in a concise and natural way.…”

Section: Modeling the Stentmentioning

confidence: 99%

“…A method to segment the stent graft in 3D by finding the optimal paths between a large set of automatically detected seed points is proposed in [66]. The method can be divided into three steps, which are illustrated in the flow chart in Figure 2.11.…”

Section: Segmentation Of the Stent Graft Via The Minimum Cost Path Mementioning

confidence: 99%

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Segmentation and motion estimation of stent grafts in abdominal aortic aneurysms

Klein¹

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Section: Segmentation Of the Stent Graftmentioning

confidence: 99%

“…It has been shown that combining a subset of all volumes in the sequence can produce better results due to a more optimal compromise between noise and motion blur [66].…”

Section: Combining the Volumesmentioning

confidence: 99%

Section: Modeling the Stentmentioning

confidence: 99%

Section: Segmentation Of the Stent Graft Via The Minimum Cost Path Mementioning

confidence: 99%

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Segmentation and motion estimation of stent grafts in abdominal aortic aneurysms

Klein¹

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“…It is a level set method in which a front is propagated monotonically following a (non-negative) cost function. The advantages of this method are that it can be implemented in a computationally efficient way, and that it can easily be modified to make it more suitable for a specific problem, see for example Klein, Renema, Kool & Slump (2009) and Cohen & Deschamps (2007). To use the MCP method for stent segmentation, it is modified such that the fronts evolve from all the seed points found in the seed point detection step.…”

Section: Finding the Optimal Pathsmentioning

confidence: 99%

Motion Calculations on Stent Grafts in AAA

Klein¹,

Renema²,

Vliet³

et al. 2011

Diagnosis, Screening and Treatment of Abdominal, Thoracoabdominal and Thoracic Aortic Aneurysms

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3D Stent Recovery from One X-Ray Projection

Demirci

Bigdelou

Wang

et al. 2011

Lecture Notes in Computer Science

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In the current clinical workflow of endovascular abdominal aortic repairs (EVAR) a stent graft is inserted into the aneurysmatic aorta under 2D angiographic imaging. Due to the missing depth information in the X-ray visualization, it is highly difficult in particular for junior physicians to place the stent graft in the preoperatively defined position within the aorta. Therefore, advanced 3D visualization of stent grafts is highly required. In this paper, we present a novel algorithm to automatically match a 3D model of the stent graft to an intraoperative 2D image showing the device. By automatic preprocessing and a globalto-local registration approach, we are able to abandon user interaction and still meet the desired robustness. The complexity of our registration scheme is reduced by a semi-simultaneous optimization strategy incorporating constraints that correspond to the geometric model of the stent graft. Via experiments on synthetic, phantom, and real interventional data, we are able to show that the presented method matches the stent graft model to the 2D image data with good accuracy.

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Automatic segmentation of the wire frame of stent grafts from CT data

Cited by 17 publications

References 53 publications

Segmentation and motion estimation of stent grafts in abdominal aortic aneurysms

Segmentation and motion estimation of stent grafts in abdominal aortic aneurysms

Motion Calculations on Stent Grafts in AAA

3D Stent Recovery from One X-Ray Projection

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