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Presenter: Andrew Gilbert, GE Healthcare
Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alternative pipeline where images are generated from existing high-quality annotations using generative adversarial networks (GANs).Annotations are derived automatically from previously built anatomical models. Annotations are transformed into realistic synthetic ultrasound images with paired labels using a CycleGAN. We demonstrate the pipeline by generating synthetic2D echocardiography images to compare with existing deep learning ultrasound segmentation datasets. The proposed pipeline opens the door for automatic generation of training data for many tasks in cardiac imaging.
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