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Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation
Citation Link: https://doi.org/10.15480/882.3039
Publikationstyp
Journal Article
Date Issued
2020-09-17
Sprache
English
Author(s)
Institut
TORE-DOI
TORE-URI
Volume
6
Issue
1
Article Number
20200015
Citation
Current Directions in Biomedical Engineering 1 (6): 20200015 (2020)
Publisher DOI
Scopus ID
Publisher
de Gruyter
© 2020 Malte Seemann et al., published by De Gruyter, Berlin/Boston 2020. Deep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.
Subjects
computed tomography angiography
data augmentation
deep learning
neural style transfer
segmentation
synthetic image data
DDC Class
570: Biowissenschaften, Biologie
610: Medizin
More Funding Information
European Regional Development Fund (ERDF)
Hamburgische Investitions- und Förderbank (IFB)
Publication version
publishedVersion
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[23645504 - Current Directions.pdf
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