22 citations · 23 across the 4 of their papers we have counts for
4 papers
sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs
Sina Amirrajab, Yasmina Al Khalil, Cristian Lorenz +3
There has been considerable interest in the MR physics-based simulation of a database of virtual cardiac MR images for the development of deep-learning analysis networks. However,…
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
Sina Amirrajab, Samaneh Abbasi-Sureshjani, Yasmina Al Khalil +4
Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with…
3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images
Cheryl Sital, Tom Brosch, Dominique Tio +2
Automatic segmentation of anatomical structures with convolutional neural networks (CNNs) constitutes a large portion of research in medical image analysis. The majority of CNN-bas…
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
Danielle F. Pace, Adrian V. Dalca, Tom Brosch +5
We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a…