Improving myocardium segmentation in cardiac CT angiography using spectral information

Steffen Bruns*, Jelmer M. Wolterink, Robbert W. van Hamersvelt, Majd Zreik, Tim Leiner, Ivana Išgum

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage Processing
EditorsElsa D. Angelini, Bennett A. Landman
Place of PublicationBellingham, WA
PublisherSPIE Press
Number of pages7
ISBN (Electronic)9781510625464
ISBN (Print)9781510625457
DOIs
Publication statusPublished - 1 Jan 2019
Externally publishedYes
EventSPIE Medical Imaging 2019: Image Processing - San Diego, United States
Duration: 19 Feb 201921 Feb 2019

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume10949
ISSN (Print)1605-7422
ISSN (Electronic)2410-9045

Conference

ConferenceSPIE Medical Imaging 2019: Image Processing
CountryUnited States
CitySan Diego
Period19/02/1921/02/19

Keywords

  • 3D convolutional neural network
  • Cardiac CT angiography
  • Data augmentation
  • Deep learning
  • Myocardium segmentation
  • Spectral CT

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