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2018 | OriginalPaper | Chapter

Combining Deep Learning and Shape Priors for Bi-Ventricular Segmentation of Volumetric Cardiac Magnetic Resonance Images

Authors : Jinming Duan, Jo Schlemper, Wenjia Bai, Timothy J. W. Dawes, Ghalib Bello, Carlo Biffi, Georgia Doumou, Antonio De Marvao, Declan P. O’Regan, Daniel Rueckert

Published in: Shape in Medical Imaging

Publisher: Springer International Publishing

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Abstract

In this paper, we combine a network-based method with image registration to develop a shape-based bi-ventricular segmentation tool for short-axis cardiac magnetic resonance (CMR) volumetric images. The method first employs a fully convolutional network (FCN) to learn the segmentation task from manually labelled ground truth CMR volumes. However, due to the presence of image artefacts in the training dataset, the resulting FCN segmentation results are often imperfect. As such, we propose a second step to refine the FCN segmentation. This step involves performing a non-rigid registration with multiple high-resolution bi-ventricular atlases, allowing the explicit shape priors to be inferred. We validate the proposed approach on 1831 healthy subjects and 200 subjects with pulmonary hypertension. Numerical experiments on the two datasets demonstrate that our approach is capable of producing accurate, high-resolution and anatomically smooth bi-ventricular models, despite the artefacts in the input CMR volumes.

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previous chapter DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images

next chapter Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

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Title: Combining Deep Learning and Shape Priors for Bi-Ventricular Segmentation of Volumetric Cardiac Magnetic Resonance Images
Authors: Jinming Duan
Jo Schlemper
Wenjia Bai
Timothy J. W. Dawes
Ghalib Bello
Carlo Biffi
Georgia Doumou
Antonio De Marvao
Declan P. O’Regan
Daniel Rueckert
Publisher: Springer International Publishing
Book: Shape in Medical Imaging
Print ISBN: 978-3-030-04746-7

Electronic ISBN: 978-3-030-04747-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-04747-4_24

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