Direct Learning Left Ventricular Meshes from CMR Images

Romaszko, L., Borowska, A. , Lazarus, A., Gao, H. , Luo, X. and Husmeier, D. (2019) Direct Learning Left Ventricular Meshes from CMR Images. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 25. ISBN 9781927877647 (doi: 10.11159/icsta19.25)

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Biomechanical studies of the left ventricle (LV) typically rely on a mesh of finite element nodes for a discrete representation of the LV geometry, which is used in an approximate numerical solution of the cardio-mechanical equations based on finite-element discretisation. This is typically done by first manually annotating cardiovascular magnetic resonance (CMR) scans, second creating a preliminary mesh, third manually correcting the mesh to account for motion. The whole process requires specialist knowledge, is time consuming and prone to human error, which prohibits its common adoption in the clinics. We propose to overcome these shortcomings by applying statistical pattern recognition techniques to CMR images. In particular, we train a convolutional neural network (CNN) to predict the LVM via learning its principal component representation directly from CMR scans. As a useful side-product we obtain a low-dimensional representation of the LVM, which is of interest for surrogate models (emulators) of the myocardium constitutive models.

Item Type:Conference Proceedings
Additional Information:Dirk Husmeier is supported by a grant from the Royal Society of Edinburgh, award number 62335.
Keywords:Statistical pattern recognition, convolutional neural networks, CMR images, left ventricular mesh, dimensionality reduction, principle component analysis.
Glasgow Author(s) Enlighten ID:Romaszko, Mr Lukasz and Luo, Professor Xiaoyu and Husmeier, Professor Dirk and Borowska, Dr Agnieszka and Gao, Dr Hao and Lazarus, Dr Alan
Authors: Romaszko, L., Borowska, A., Lazarus, A., Gao, H., Luo, X., and Husmeier, D.
Subjects:Q Science > QA Mathematics
College/School:College of Science and Engineering > School of Mathematics and Statistics > Mathematics
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Copyright Holders:Copyright © 2019 International ASET Inc.
First Published:First published in Proceedings of the International Conference on Statistics: Theory and Applications (ICSTA’19): 25
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
694461EPSRC Centre for Multiscale soft tissue mechanics with application to heart & cancerRaymond OgdenEngineering and Physical Sciences Research Council (EPSRC)EP/N014642/1M&S - MATHEMATICS