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Segmentation of Abdominal Aortic Aneurysms in CT Images Using a Radial Model Approach

机译:径向模型方法分割CT图像中的腹主动脉瘤

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Abdominal Aortic Aneurysm (AAA) is a dangerous condition where the weakening of the aortic wall leads to its deformation and the generation of a thrombus. To prevent a possible rupture of the aortic wall, AAAs can be treated non-invasively by means of the Endovascular Aneurysm Repair technique (EVAR), which consists of placing a stent-graft inside the aorta in order to exclude the bulge from the blood circulation and usually leads to its contraction. Nevertheless, the bulge may continue to grow without any apparent leak. In order to effectively assess the changes experienced after surgery, it is necessary to segment the aneurysm, which is a very time-consuming task. Here we describe the initial results of a novel model-based approach for the semi-automatic segmentation of both the lumen and the thrombus of AAAs, using radial functions constrained by a priori knowledge and spatial coherency.
机译:腹主动脉瘤(AAA)是一种危险情况,主动脉壁变薄会导致其变形并形成血栓。为防止主动脉壁可能破裂,可通过血管内动脉瘤修复技术(EVAR)对AAA进行无创治疗,该技术包括将支架植入物放置在主动脉内,以排除血液循环中的隆起通常会导致其收缩但是,凸起可能会继续增长而没有任何明显的泄漏。为了有效评估手术后的变化,有必要对动脉瘤进行分割,这是一项非常耗时的任务。在这里,我们描述了使用基于先验知识和空间相干性约束的径向函数对AAA的内腔和血栓进行半自动分割的基于模型的新方法的初步结果。

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