Deep Learning for Recognition of Endoleak After Endovascular Abdominal Aortic Aneurysm Repair

摘要

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.