22 citations · 42 across the 5 of their papers we have counts for
5 papers
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs
Sina Amirrajab, Cristian Lorenz, Juergen Weese +2
We propose a method for synthesizing cardiac magnetic resonance (MR) images with plausible heart pathologies and realistic appearances for the purpose of generating labeled data fo…
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…
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model
Samaneh Abbasi-Sureshjani, Sina Amirrajab, Cristian Lorenz +3
We propose a hybrid controllable image generation method to synthesize anatomically meaningful 3D+t labeled Cardiac Magnetic Resonance (CMR) images. Our hybrid method takes the mec…
Deep Learning Based Rib Centerline Extraction and Labeling
Matthias Lenga, Tobias Klinder, Christian Bürger +3
Automated extraction and labeling of rib centerlines is a typically needed prerequisite for more advanced assisted reading tools that help the radiologist to efficiently inspect al…