A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of
  3D Echocardiography Images using a GAN
Cristiana Tiago Andrew Gilbert Ahmed S. Beela Svein Arne Aase Sten Roar Snare Jurica Sprem
Due to privacy issues and limited amount of publicly available labeled
datasets in the domain of medical imaging, we propose an image generation
pipeline to synthesize 3D echocardiographic images with corresponding ground
truth labels, to alleviate the need for data collection and for laborious and
error-prone human labeling of images for subsequent Deep Learning (DL) tasks.
The proposed method utilizes detailed anatomical segmentations of the heart as
ground truth label sources. This initial dataset is combined with a second
dataset made up of real 3D echocardiographic images to train a Generative
Adversarial Network (GAN) to synthesize realistic 3D cardiovascular Ultrasound
images paired with ground truth labels. To generate the synthetic 3D dataset,
the trained GAN uses high resolution anatomical models from Computed Tomography
(CT) as input. A qualitative analysis of the synthesized images showed that the
main structures of the heart are well delineated and closely follow the labels
obtained from the anatomical models. To assess the usability of these synthetic
images for DL tasks, segmentation algorithms were trained to delineate the left
ventricle, left atrium, and myocardium. A quantitative analysis of the 3D
segmentations given by the models trained with the synthetic images indicated
the potential use of this GAN approach to generate 3D synthetic data, use the
data to train DL models for different clinical tasks, and therefore tackle the
problem of scarcity of 3D labeled echocardiography datasets.