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IVUS-Simulation for Improving Segmentation Performance of Neural Networks via Data Augmentation
Publikationstyp
Conference Paper
Date Issued
2019-09
Sprache
English
Author(s)
Institut
TORE-URI
Start Page
47
End Page
51
Citation
CURAC 2019 - Tagungsband : 18. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie e.V.: 47-51
Contribution to Conference
Convolutional neural networks (CNNs) produce promising results when applied to a wide range of medical imaging tasks including the segmentation of tissue structures like the artery lumen and wall layers in intravascular ultrasound (IVUS) image data. However, large annotated datasets are needed for training to achieve sufficient performances. To increase the dataset size, data augmentation techniques like random image transformations are commonly used. In this work, we propose a new systematic approach to generate artificial IVUS image data with the ultrasound simulation software Field II in order to perform data augmentation. A simulation model was systematically tuned to a clinical data set based on the Frechet Inception Distance (FID). We show that the segmentation performance of a state\-of\-the\-art CNN with U\-Net architecture improves when pre\-trained with our synthetic IVUS data
DDC Class
004: Computer Sciences