naa a22 4500 510808301 CHVBK 20180411083428.0 cr unu---uuuuu 180411e20130301xx s 000 0 eng 10.1007/s11548-012-0766-6 doi (NATIONALLICENCE)springer-10.1007/s11548-012-0766-6 Vascular decomposition using weighted approximate convex decomposition [Elektronische Daten] [Ashirwad Chowriappa, T. Kesavadas, Maxim Mokin, Peter Kan, Sarthak Salunke, Sabareesh Natarajan, Peter Scott] Objective: Stroke treatment often requires analysis of vascular pathology evaluated using computed tomography (CT) angiography. Due to vascular variability and complexity, finding precise relationships between vessel geometries and arterial pathology is difficult. A new convex shape decomposition strategy was developed to understand complex vascular structures and synthesize a weighted approximate convex decomposition (WACD) method for vascular decomposition in computer-aided diagnosis. Materials and methods: The vascular tree is decomposed into optimal number of components (determined by an expert). The decomposition is based on two primary features of vascular structures: (i) the branching factor that allows structural decomposition and (ii) the concavity over the vessel surface seen primarily at the site of an aneurysm. Such surfaces are decomposed into subcomponents. Vascular sections are reconstructed using CT angiograms. Next the dual graph is constructed, and edge weights for the graph are computed from shape indices. Graph vertices are iteratively clustered by a mesh decimation operator, while minimizing a cost function related to concavity. Results: The method was validated by first comparing results with an approximate convex decomposition (ACD) method and next on vessel sections (n =177) whose number of clusters (ground truth) was predetermined by an expert. In both cases, WACD produced promising results with 84.7% of the vessel sections correctly clustered and when compared with ACD produced a more effective decomposition. Next, the algorithm was validated in a longitudinal study data of 4 subjects where volumetric and surface area comparisons were made between expert segmented sections and WACD decomposed sections that contained aneurysms. The results showed a mean error rate of 7.8% for volumetric comparisons and 10.4% for surface area comparisons. Conclusion: Decomposition of the cerebral vasculature from CT angiograms into a geometrically optimal set of convex regions may be useful for computer-assisted diagnosis. A new WACD method capable of decomposing complex vessel structures, including bifurcations and aneurysms, was developed and tested with promising results. CARS, 2012 Convex decomposition nationallicence Vascular segmentation nationallicence Aneurysm nationallicence Bifurcation segmentation nationallicence Curvature nationallicence Shape index nationallicence Computer-aided diagnosis nationallicence Chowriappa Ashirwad Department of Computer Science and Engineering, The State University of New York, Buffalo, NY, USA aut Kesavadas T. Department of Mechanical and Aerospace Engineering, The State University of New York, Buffalo, NY, USA aut Mokin Maxim Department of Neuroscience, Millard Fillmore Gates Hospital, Buffalo, NY, USA aut Kan Peter Department of Neuroscience, Millard Fillmore Gates Hospital, Buffalo, NY, USA aut Salunke Sarthak Department of Mechanical and Aerospace Engineering, The State University of New York, Buffalo, NY, USA aut Natarajan Sabareesh Department of Neuroscience, Millard Fillmore Gates Hospital, Buffalo, NY, USA aut Scott Peter Department of Computer Science and Engineering, The State University of New York, Buffalo, NY, USA aut International Journal of Computer Assisted Radiology and Surgery Springer-Verlag 8/2(2013-03-01), 207-219 1861-6410 8:2<207 2013 8 11548 https://doi.org/10.1007/s11548-012-0766-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-012-0766-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chowriappa Ashirwad Department of Computer Science and Engineering, The State University of New York, Buffalo, NY, USA aut NATIONALLICENCE 700 1- Kesavadas T. Department of Mechanical and Aerospace Engineering, The State University of New York, Buffalo, NY, USA aut NATIONALLICENCE 700 1- Mokin Maxim Department of Neuroscience, Millard Fillmore Gates Hospital, Buffalo, NY, USA aut NATIONALLICENCE 700 1- Kan Peter Department of Neuroscience, Millard Fillmore Gates Hospital, Buffalo, NY, USA aut NATIONALLICENCE 700 1- Salunke Sarthak Department of Mechanical and Aerospace Engineering, The State University of New York, Buffalo, NY, USA aut NATIONALLICENCE 700 1- Natarajan Sabareesh Department of Neuroscience, Millard Fillmore Gates Hospital, Buffalo, NY, USA aut NATIONALLICENCE 700 1- Scott Peter Department of Computer Science and Engineering, The State University of New York, Buffalo, NY, USA aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer-Verlag 8/2(2013-03-01), 207-219 1861-6410 8:2<207 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer