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Abstract #3244

Generating Synthetic Late-Gadolinium Enhancement Images for Training Scar Segmentation Networks

Isabel Margolis1, Laura Dal Toso1, Stefano Buoso1, and Sebastian Kozerke1
1Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland

Synopsis

Keywords: Diagnosis/Prediction, Analysis/Processing, image synthesis

Motivation: The quality and accuracy of scar segmentation networks is heavily dependent on the size of the training dataset.

Goal(s): To generate a synthetic dataset of LGE images to address the scarcity of annotated in-vivo datasets.

Approach: Two conditional GANs generated synthetic labels (healthy myocardium and scar) and realistic LGE signals, forming a dataset to train and evaluate nnU-Net models against in-vivo and combined data.

Results: The network trained on only synthetic data and tested on in-vivo LGE images, achieved a Dice score of 0.77, higher than that of the network trained on only in-vivo data (0.75).

Impact: This study presents a GAN-based framework to generate high-variability synthetic LGE images for scar segmentation, achieving realistic scar patterns and outperforming real-image networks and standard augmentation.

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