DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Training Approach
Egileak: Luis Kabongo Nerea Lete Urzelai Grégory Maclair Mario Ceresa Ainhoa García-Familiar Miguel Ángel González Ballester
Data: 10.09.2017
Abstract
Computerized Tomography Angiography (CTA) based assessment of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential during follow-up to evaluate the progress of the patient along time, comparing it to the preoperative situation, and to detect complications. In this context, accurate assessment of the aneurysm or thrombus volume pre- and post-operatively is required. However, a quantifiable and trustworthy evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose an automatic pipeline for thrombus volume assessment, starting from its segmentation based on a Deep Convolutional Neural Network (DCNN) both pre-operatively and
post-operatively. The aim is to investigate several training approaches to evaluate their influence in the thrombus volume characterization.
BIB_text
title = {DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Training Approach},
pages = {29-38},
volume = {10552},
keywds = {
AAA, EVAR, Thrombus, Segmentation, DCNN, Volume
}
abstract = {
Computerized Tomography Angiography (CTA) based assessment of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential during follow-up to evaluate the progress of the patient along time, comparing it to the preoperative situation, and to detect complications. In this context, accurate assessment of the aneurysm or thrombus volume pre- and post-operatively is required. However, a quantifiable and trustworthy evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose an automatic pipeline for thrombus volume assessment, starting from its segmentation based on a Deep Convolutional Neural Network (DCNN) both pre-operatively and
post-operatively. The aim is to investigate several training approaches to evaluate their influence in the thrombus volume characterization.
}
isbn = {978-3-319-67533-6},
isi = {1},
doi = {10.1007/978-3-319-67534-3_4},
date = {2017-09-10},
year = {2017},
}
