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Automatic detection of aorto-femoral vessel trajectory from whole-body computed tomography angiography data sets

Overview of attention for article published in The International Journal of Cardiovascular Imaging, May 2016
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Title
Automatic detection of aorto-femoral vessel trajectory from whole-body computed tomography angiography data sets
Published in
The International Journal of Cardiovascular Imaging, May 2016
DOI 10.1007/s10554-016-0901-5
Pubmed ID
Authors

Xinpei Gao, Pieter H. Kitslaar, Ricardo P. J. Budde, Shengxian Tu, Michiel A. de Graaf, Liang Xu, Bo Xu, Arthur J. H. A. Scholte, Jouke Dijkstra, Johan H. C. Reiber

Abstract

Extraction of the aorto-femoral vessel trajectory is important to utilize computed tomography angiography (CTA) in an integrated workflow of the image-guided work-up prior to trans-catheter aortic valve replacement (TAVR). The aim of this study was to develop a new, fully-automated technique for the extraction of the entire arterial access route from the femoral artery to the aortic root. An automatic vessel tracking algorithm was first used to find the centerline that connected the femoral accessing points and the aortic root. Subsequently, a deformable 3D-model fitting method was used to delineate the lumen boundary of the vascular trajectory in the whole-body CTA dataset. A validation was carried out by comparing the automatically obtained results with semi-automatically obtained results from two experienced observers. The whole framework was validated on whole body CTA datasets of 36 patients. The average Dice similarity indexes between the segmentations of the automatic method and observer 1 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta were 0.977 ± 0.030, 0.980 ± 0.019, 0.982 ± 0.016; the average Dice similarity indexes between the segmentations of the automatic method and observer 2 were 0.950 ± 0.040, 0.954 ± 0.031 and 0.965 ± 0.019, respectively. The inter-observer variability resulted in a Dice similarity index of 0.954 ± 0.038, 0.952 ± 0.031 and 0.969 ± 0.018 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta. The average minimal luminal diameters (MLDs) of the ilio-femoral artery were 6.03 ± 1.48, 5.70 ± 1.43 and 5.52 ± 1.32 mm for the automatic method, observer 1 and observer 2 respectively. The MLDs of the aorta were 13.43 ± 2.54, 12.40 ± 2.93 and 12.08 ± 2.40 mm for the automatic method, observer 1 and observer 2 respectively. The automatic measurement overestimated the MLD slightly in the ilio-femoral artery at the average by 0.323 mm (SD = 0.49 mm, p < 0.001) compared to observer 1 and by 0.51 mm (SD = 0.71 mm, p < 0.001) compared to observer 2. The proposed segmentation approach can automatically provide reliable measurements of the entire arterial accessing route that can be used to support TAVR procedures. To the best of our knowledges, this approach is the first fully automatic segmentation method of the whole aorto-femoral vessel trajectory in CTA images.

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Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 14%
Researcher 5 14%
Student > Bachelor 3 8%
Lecturer 2 6%
Student > Postgraduate 2 6%
Other 7 19%
Unknown 12 33%
Readers by discipline Count As %
Medicine and Dentistry 10 28%
Agricultural and Biological Sciences 3 8%
Engineering 3 8%
Chemistry 2 6%
Computer Science 2 6%
Other 3 8%
Unknown 13 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 May 2016.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from The International Journal of Cardiovascular Imaging
#1,460
of 2,012 outputs
Outputs of similar age
#304,756
of 348,858 outputs
Outputs of similar age from The International Journal of Cardiovascular Imaging
#30
of 45 outputs
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