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首页> 外文会议>IEEE International Symposium on Biomedical Imaging >Deep Learning for Recognition of Endoleak After Endovascular Abdominal Aortic Aneurysm Repair
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Deep Learning for Recognition of Endoleak After Endovascular Abdominal Aortic Aneurysm Repair

机译：腹血管腹主动脉瘤修复后腹腔内肿瘤识别深度学习

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An Abdominal Aortic Aneurysm (AAA) is an enlarged area in the lower part of the aorta and in the case of larger or rapidly growing aneurysms represents a major surgical risk. Surgical treatment can involve open repair to replace the aneurysmal aorta with a graft or more commonly endovascular repair (EVAR) to seal an aneurysm with a stent-graft. This paper is primarily concerned with the automated binary classification of Endoleaks, defined as perigraft flow into the residual aneurysm sac, within computerized tomography angiography (CTA) volumes of patients post-EVAR. We propose a set of cascaded deep convolutional neural network architectures to localize an aneurysm region and subsequently predict the presence of an Endoleak within this region. The proposed method has further shown promising results on our dataset of over 700 labeled CTA volumes, with an optimized accuracy of 89 ± 3% on the task of Endoleak recognition.
机译：腹主动脉瘤（AAA）是主动脉下部的扩大区域，并且在较大或快速生长的动脉瘤代表主要手术风险的情况下。手术治疗可以涉及开放修复以用移植物或更常见的血管内修复（EVAR）替换动脉瘤主动脉，以用支架移植物密封动脉瘤。本文主要涉及eNDOLEAKS的自动二进制分类，定义为eVAR后的计算机断层摄影血管造影（CTA）患者中的患者流入残留的动脉瘤SAC。我们提出了一组级联的深度卷积神经网络架构来定位动脉瘤区域，随后预测该区域内的螺注嘴。所提出的方法进一步显示了我们在700多个标记的CTA卷上的数据集上的有希望的结果，优化精度为89±3％的endoleak识别的任务。

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