Xu, H, Zacur, E, Schneider, JE et al. (1 more author) (2019) Ventricle Surface Reconstruction from Cardiac MR Slices Using Deep Learning. In: Coudière, Y, Ozenne, V, Vigmond, E and Zemzemi, N, (eds.) Lecture Notes in Computer Science. FIMH 2019: Functional Imaging and Modeling of the Heart, 06-08 Jun 2019, Bordeaux, France. Springer Verlag , pp. 342-351. ISBN 9783030219482
Abstract
Reconstructing 3D ventricular surfaces from 2D cardiac MR data is challenging due to the sparsity of the input data and the presence of interslice misalignment. It is usually formulated as a 3D mesh fitting problem often incorporating shape priors and smoothness regularization, which might affect accuracy when handling pathological cases. We propose to formulate the 3D reconstruction as a volumetric mapping problem followed by isosurfacing from dense volumetric data. Taking advantage of deep learning algorithms, which learn to predict each voxel label without explicitly defining the shapes, our method is capable of generating anatomically meaningful surfaces with great flexibility. The sparse 3D volumetric input can process contours with any orientations and thus can utilize information from multiple short- and long-axis views. In addition, our method can provide correction of motion artifacts. We have validated our method using a statistical shape model on reconstructing 3D shapes from both spatially consistent and misaligned input data.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2019. This is a post-peer-review, pre-copyedit version of an conference paper published in Functional Imaging and Modeling of the Heart . The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-21949-9_37. |
Keywords: | Mesh reconstruction; Cardiac MRI; Deep learning |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Biomedical Imaging Science Dept (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Jun 2019 13:06 |
Last Modified: | 01 Jul 2019 13:08 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-030-21949-9_37 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:147863 |