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Improving myocardium segmentation in cardiac CT angiography using spectral information
Author(s): Steffen Bruns; Jelmer M. Wolterink; Robbert W. van Hamersvelt; Majd Zreik; Tim Leiner; Ivana Išgum
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Paper Abstract

Accurate segmentation of the left ventricle myocardium in cardiac CT angiography (CCTA) is essential for e.g. the assessment of myocardial perfusion. Automatic deep learning methods for segmentation in CCTA might suffer from differences in contrast-agent attenuation between training and test data due to non-standardized contrast administration protocols and varying cardiac output. We propose augmentation of the training data with virtual mono-energetic reconstructions from a spectral CT scanner which show different attenuation levels of the contrast agent. We compare this to an augmentation by linear scaling of all intensity values, and combine both types of augmentation. We train a 3D fully convolutional network (FCN) with 10 conventional CCTA images and corresponding virtual mono-energetic reconstructions acquired on a spectral CT scanner, and evaluate on 40 CCTA scans acquired on a conventional CT scanner. We show that training with data augmentation using virtual mono-energetic images improves upon training with only conventional images (Dice similarity coefficient (DSC) 0.895 ± 0.039 vs. 0.846 ± 0.125). In comparison, training with data augmentation using linear scaling improves the DSC to 0.890 ± 0.039. Moreover, combining the results of both augmentation methods leads to a DSC of 0.901 ± 0.036, showing that both augmentations lead to different local improvements of the segmentations. Our results indicate that virtual mono-energetic images improve the generalization of an FCN used for myocardium segmentation in CCTA images.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492M (15 March 2019); doi: 10.1117/12.2512229
Show Author Affiliations
Steffen Bruns, Univ. Medical Ctr. Utrecht (Netherlands)
Jelmer M. Wolterink, Univ. Medical Ctr. Utrecht (Netherlands)
Robbert W. van Hamersvelt, Univ. Medical Ctr. Utrecht (Netherlands)
Majd Zreik, Univ. Medical Ctr. Utrecht (Netherlands)
Tim Leiner, Univ. Medical Ctr. Utrecht (Netherlands)
Ivana Išgum, Univ. Medical Ctr. Utrecht (Netherlands)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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