Xu, H, Schneider, JE and Grau, V (2019) Calculation of Anatomical and Functional Metrics Using Deep Learning in Cardiac MRI: Comparison Between Direct and Segmentation-Based Estimation. In: Lecture Notes in Computer Science. Statistical Atlases and Computational Modelling of the Heart Workshop (STACOM 2018), 16 Sep 2018, Granada, Spain. Springer , pp. 402-411. ISBN 9783030120283
Abstract
In this paper we propose a collection of left ventricle (LV) quantification methods using different versions of a common neural network architecture. In particular, we compare the accuracy obtained with direct calculation (regression) of the desired metrics, a segmentation network and a novel combined approach. We also introduce temporal dynamics through the use of a Long Short-Term Memory (LSTM) network. We train and evaluate our methods on MICCAI 2018 Left Ventricle Full Quantification Challenge dataset. We perform 5-fold cross-validation on the training dataset and compare our results with the state-of-the-art methods evaluated on the same dataset. In our experiments, segmentation-based methods outperform all the other variants as well as current state of the art. The introduction of LSTM does produces only minor improvements in accuracy. The novel segmentation/estimation network improves the results on estimation-only but does not reach the accuracy of segmentation-based metric calculation.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Left ventricle quantification; Deep learning |
Dates: |
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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: | 07 May 2019 16:05 |
Last Modified: | 07 May 2019 16:05 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-12029-0_43 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145516 |