A comparison of two methods to segment stent grafts in CT data

Almar Klein; Michel Klaassen; Luuk J. Oostveen; J. Adam van der Vliet; Yvonne Hoogeveen; Leo J. Schultze Kool M.D.; W. KlaasJan Renema; Cornelis H. Slump

doi:10.1117/12.911296

13 April 2012 A comparison of two methods to segment stent grafts in CT data

Almar Klein, Michel Klaassen, Luuk J. Oostveen, J. Adam van der Vliet, Yvonne Hoogeveen, Leo J. Schultze Kool M.D., W. KlaasJan Renema, Cornelis H. Slump

Author Affiliations +

Proceedings Volume 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging; 83170H (2012) https://doi.org/10.1117/12.911296
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Late stent graft failure is a serious complication in endovascular repair of aortic aneurysms. Better understanding of the motion characteristics of stent grafts will be beneficial for designing future devices. In addition, analysis of stent graft movement in individual patients in vivo can be valuable for predicting stent graft failure in these patients. To be able to gather information on stent graft motion in a quick and robust fashion, an automatic segmentation method is required. In this work we compare two segmentation methods that produce a geometric model in the form of an undirected graph. The first method tracks along the centerline of the stent and segments the stent in 2D slices sampled orthogonal to it. The second method used a modified version of the minimum cost path (MCP) method to segment the stent directly in 3D. Using annotated reference data both methods were evaluated in an experiment. The results show that the centerline-based method and the MCP-based method have an accuracy of approximately 65% and 92%, respectively. The difference in accuracy can be explained by the fact that the centerline method makes assumptions about the topology of the stent which do not always hold in practice. This causes difficulties that are hard and sometimes impossible to overcome. In contrast, the MCP-based method works directly in 3D and is capable of segmenting a large variety of stent shapes and stent types.

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Almar Klein, Michel Klaassen, Luuk J. Oostveen, J. Adam van der Vliet, Yvonne Hoogeveen, Leo J. Schultze Kool M.D., W. KlaasJan Renema, and Cornelis H. Slump "A comparison of two methods to segment stent grafts in CT data", Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 83170H (13 April 2012); https://doi.org/10.1117/12.911296

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KEYWORDS

3D modeling

Data modeling

Image segmentation

Microchannel plates

Computed tomography

3D image processing

Detection and tracking algorithms

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