Vicomtech, Visual Communications Technologies Centre, Spain.
Comput Biol Med. 2011 Oct;41(10):871-80. doi: 10.1016/j.compbiomed.2011.07.005.
Abdominal aortic aneurysm (AAA) is a condition where the weakening of the aortic wall leads to its widening and the generation of a thrombus. To prevent a possible rupture of the aortic wall, AAA can be treated non-invasively by means of the endovascular aneurysm repair technique (EVAR), consisting of placing a stent-graft inside the aorta by a cateter to exclude the aneurysm sac from the blood circulation. A major complication is the presence of liquid blood turbulences, called endoleaks, in the thrombus formed in the space between the aortic wall and the stent-graft. In this paper we propose an automatic method for the detection of type II endoleaks in computer tomography angiography (CTA) images. The lumen and thrombus in the aneurysm area are first segmented using a radial model approach. Then, these regions are split into Thrombus Connected Components (TCCs) using a watershed-based segmentation and geometric and image content-based characteristics are obtained for each TCC. Finally, TCCs are classified into endoleaks and non-endoleaks using a multilayer Perceptron (MLP) trained on manual labeled sample TCCs provided by experts.
腹主动脉瘤 (AAA) 是一种由于主动脉壁变弱导致其扩张并形成血栓的疾病。为了防止主动脉壁可能破裂，可以通过血管内动脉瘤修复技术 (EVAR) 对 AAA 进行非侵入性治疗，该技术通过导管将支架移植物放置在主动脉内，将动脉瘤囊从血液循环中排除。一种主要的并发症是在主动脉壁和支架移植物之间形成的血栓中存在称为内漏的液体血液湍流。在本文中，我们提出了一种用于在计算机断层血管造影 (CTA) 图像中检测 II 型内漏的自动方法。首先使用径向模型方法对动脉瘤区域的管腔和血栓进行分割。然后，使用基于分水岭的分割将这些区域分割为血栓连通组件 (TCC)，并为每个 TCC 获取几何和图像内容特征。最后，使用多层感知器 (MLP) 对专家提供的手动标记样本 TCC 进行训练，将 TCC 分类为内漏和非内漏。
