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DeepCS: Deep Convolutional Neural Network and SVM Based Single Image Super-Resolution Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach

Authors : A. Virzì, P. Gori, C. O. Muller, E. Mille, Q. Peyrot, L. Berteloot, N. Boddaert, S. Sarnacki, I. Bloch

Published in: Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

Publisher: Springer International Publishing

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Abstract

In this paper, we propose a patch-based deep learning approach to segment pelvic vessels in 3D MRI images of pediatric patients. For a given T2 weighted MRI volume, a set of 2D axial patches are extracted using a limited number of user-selected landmarks. In order to take into account the volumetric information, successive 2D axial patches are combined together, producing a set of pseudo RGB color images. These RGB images are then used as input for a convolutional neural network (CNN), pre-trained on the ImageNet dataset, which results into both segmentation and vessel labeling as veins or arteries. The proposed method is evaluated on 35 MRI volumes of pediatric patients, obtaining an average segmentation accuracy in terms of Average Symmetric Surface Distance of \(ASSD = 0.89 \pm 0.07\) mm and Dice Index of \(DC = 0.79 \pm 0.02\).

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previous chapter Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach
next chapter Multi-view Image Reconstruction: Application to Fetal Ultrasound Compounding
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Metadata
Title
Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach
Authors
A. Virzì
P. Gori
C. O. Muller
E. Mille
Q. Peyrot
L. Berteloot
N. Boddaert
S. Sarnacki
I. Bloch
Copyright Year
2018
Book
Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

Print ISBN: 978-3-030-00806-2

Electronic ISBN: 978-3-030-00807-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00807-9_10

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