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Robust Detection of Circles in the Vessel Contours and Application to Local Probability Density Estimation Read chapter Read first chapter

2017 | Supplement | Chapter

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

Authors : Karen López-Linares, Luis Kabongo, Nerea Lete, Gregory Maclair, Mario Ceresa, Ainhoa García-Familiar, Iván Macía, Miguel Ángel González Ballester

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

Publisher: Springer International Publishing

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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 pre-operative 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.

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previous chapter Robust Automatic Graph-Based Skeletonization of Hepatic Vascular Trees
next chapter Vascular Segmentation in TOF MRA Images of the Brain Using a Deep Convolutional Neural Network
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Metadata
Title
DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Training Approach
Authors
Karen López-Linares
Luis Kabongo
Nerea Lete
Gregory Maclair
Mario Ceresa
Ainhoa García-Familiar
Iván Macía
Miguel Ángel González Ballester
Copyright Year
2017
Book
Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

Print ISBN: 978-3-319-67533-6

Electronic ISBN: 978-3-319-67534-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-67534-3_4

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