Detection of type II endoleaks in abdominal aortic aneurysms after endovascular repair

@Article {
author = {Iván Macía and Manuel Graña and Josu Maiora and Mariano de Blas},
title = {Detection of type II endoleaks in abdominal aortic aneurysms after endovascular repair},
journal = {Computers in Medicine and Biology},
pages = {871-889},
volume = {41},
keywds = {
Abdominal aortic aneurysm, Endovascular aneurysm repair, Endoleak detection, Thrombus segmentation, Neural networks, Image processing
}
abstract = {
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.
}
isi = {1},
date = {2011-10-01},
year = {2011},
}