Int J Comput Assist Radiol Surg - Vascular decomposition using weighted approximate convex decomposition.

Tópicos

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Resumo

JECTIVE: 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.

Resumo Limpo

jectiv stroke treatment often requir analysi vascular patholog evalu use comput tomographi ct angiographi due vascular variabl complex find precis relationship vessel geometri arteri patholog difficult new convex shape decomposit strategi develop understand complex vascular structur synthes weight approxim convex decomposit wacd method vascular decomposit computeraid diagnosismateri method vascular tree decompos optim number compon determin expert decomposit base two primari featur vascular structur branch factor allow structur decomposit ii concav vessel surfac seen primarili site aneurysm surfac decompos subcompon vascular section reconstruct use ct angiogram next dual graph construct edg weight graph comput shape indic graph vertic iter cluster mesh decim oper minim cost function relat concavityresult method valid first compar result approxim convex decomposit acd method next vessel section n whose number cluster ground truth predetermin expert case wacd produc promis result vessel section correct cluster compar acd produc effect decomposit next algorithm valid longitudin studi data subject volumetr surfac area comparison made expert segment section wacd decompos section contain aneurysm result show mean error rate volumetr comparison surfac area comparisonsconclus decomposit cerebr vasculatur ct angiogram geometr optim set convex region may use computerassist diagnosi new wacd method capabl decompos complex vessel structur includ bifurc aneurysm develop test promis result

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