DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Training Approach

Authors: Karen López-Linares Román Luis Kabongo Nerea Lete Urzelai Grégory Maclair Mario Ceresa Ainhoa García-Familiar Iván Macía Oliver Miguel Ángel González Ballester

Date: 10.09.2017


PDF

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

@Article {
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},
}
Vicomtech

Parque Científico y Tecnológico de Gipuzkoa,
Paseo Mikeletegi 57,
20009 Donostia / San Sebastián (Spain)

+(34) 943 309 230

close overlay