21 March 2016 Precise renal artery segmentation for estimation of renal vascular dominant regions
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Abstract
This paper presents a novel renal artery segmentation method combining graph-cut and template-based tracking methods and its application to estimation of renal vascular dominant region. For the purpose of giving a computer assisted diagnose for kidney surgery planning, it is important to obtain the correct topological structures of renal artery for estimation of renal vascular dominant regions. Renal artery has a low contrast, and its precise extraction is a difficult task. Previous method utilizing vesselness measure based on Hessian analysis, still cannot extract the tiny blood vessels in low-contrast area. Although model-based methods including superellipsoid model or cylindrical intensity model are low-contrast sensitive to the tiny blood vessels, problems including over-segmentation and poor bifurcations detection still remain. In this paper, we propose a novel blood vessel segmentation method combining a new Hessian-based graph-cut and template modeling tracking method. Firstly, graph-cut algorithm is utilized to obtain the rough segmentation result. Then template model tracking method is utilized to improve the accuracy of tiny blood vessel segmentation result. Rough segmentation utilizing graph-cut solves the bifurcations detection problem effectively. Precise segmentation utilizing template model tracking focuses on the segmentation of tiny blood vessels. By combining these two approaches, our proposed method segmented 70% of the renal artery of 1mm in diameter or larger. In addition, we demonstrate such precise segmentation can contribute to divide renal regions into a set of blood vessel dominant regions utilizing Voronoi diagram method.
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Chenglong Wang, Mitsuru Kagajo, Yoshihiko Nakamura, Masahiro Oda, Yasushi Yoshino, Tokunori Yamamoto, Kensaku Mori, "Precise renal artery segmentation for estimation of renal vascular dominant regions", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842M (21 March 2016); doi: 10.1117/12.2217492; https://doi.org/10.1117/12.2217492
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