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

Mota

Inproceedings

Data

2017-09-10

Egileak

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

Liburua

Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

@Inproceedings{
author ={Karen López-Linares Román, Luis Kabongo, Nerea Lete Urzelai, Grégory Maclair, Mario Ceresa, Ainhoa García-Familiar, Iván Macía Oliver, Miguel Ángel González Ballester},
title ={DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Training Approach},
booktitle ={Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis},
publisher ={Springer International Publishing AG 2017},
editor ={M. J. Cardoso et al.},
address ={Quebec City, Canada},
date ={2017-09-10},
year ={2017},
pages ={29-38},
volume ={10552},
keys ={

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 ={Yes}
}