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SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing
Citation Link: https://doi.org/10.15480/882.2976
Publikationstyp
Journal Article
Date Issued
2020-06-18
Sprache
English
Author(s)
Bargsten, Lennart
Institut
TORE-DOI
TORE-URI
Volume
15
Issue
9
Start Page
1427
End Page
1436
Citation
International Journal of Computer Assisted Radiology and Surgery 9 (15): 1427-1436 (2020-09-01)
Publisher DOI
Scopus ID
PubMed ID
32556953
Publisher
Springer
Purpose: In the field of medical image analysis, deep learning methods gained huge attention over the last years. This can be explained by their often improved performance compared to classic explicit algorithms. In order to work well, they need large amounts of annotated data for supervised learning, but these are often not available in the case of medical image data. One way to overcome this limitation is to generate synthetic training data, e.g., by performing simulations to artificially augment the dataset. However, simulations require domain knowledge and are limited by the complexity of the underlying physical model. Another method to perform data augmentation is the generation of images by means of neural networks. Methods: We developed a new algorithm for generation of synthetic medical images exhibiting speckle noise via generative adversarial networks (GANs). Key ingredient is a speckle layer, which can be incorporated into a neural network in order to add realistic and domain-dependent speckle. We call the resulting GAN architecture SpeckleGAN. Results: We compared our new approach to an equivalent GAN without speckle layer. SpeckleGAN was able to generate ultrasound images with very crisp speckle patterns in contrast to the baseline GAN, even for small datasets of 50 images. SpeckleGAN outperformed the baseline GAN by up to 165 % with respect to the Fréchet Inception distance. For artery layer and lumen segmentation, a performance improvement of up to 4 % was obtained for small datasets, when these were augmented with images by SpeckleGAN. Conclusion: SpeckleGAN facilitates the generation of realistic synthetic ultrasound images to augment small training sets for deep learning based image processing. Its application is not restricted to ultrasound images but could be used for every imaging methodology that produces images with speckle such as optical coherence tomography or radar.
Subjects
Deep learning
Image segmentation
Small datasets
Speckle noise
Synthetic image generation
Theory-guided neural networks
DDC Class
004: Informatik
610: Medizin
Funding(s)
More Funding Information
This work was partially funded by the European Regional Development Fund (ERDF), by the Hamburgische Investitions- und Förderbank (IFB) and by the Free and Hanseatic City of Hamburg.
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