Vessel tractography using an intensity based tensor model with branch detection

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Çetin, Süheyla and Demir, Ali and Yezzi, Anthony and Değertekin, Muzaffer and Ünal, Gözde (2013) Vessel tractography using an intensity based tensor model with branch detection. IEEE Transactions on Medical Imaging, 32 (2). pp. 348-363. ISSN 0278-0062 (Print) 1558-254X (Online)

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Official URL: http://dx.doi.org/10.1109/TMI.2012.2227118


In this paper, we present a tubular structure seg- mentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmen- tation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient Computed Tomography Angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert.

Item Type:Article
Uncontrolled Keywords:Branch detection; computed tomography angiography (CTA); coronary arteries; segmentation; tensor estimation; tractography; tubular structures; vessel trees
Subjects:Q Science > QA Mathematics > QA075 Electronic computers. Computer science
R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
ID Code:21672
Deposited By:Gözde Ünal
Deposited On:08 Jul 2013 15:22
Last Modified:01 Aug 2019 10:38

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