sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs

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, the employment of such a database is limited or shows suboptimal performance due to the realism gap, missing textures, a...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-08
Main Authors: Amirrajab, Sina, Yasmina Al Khalil, Lorenz, Cristian, Weese, Jurgen, Pluim, Josien, Breeuwer, Marcel
Format: Article
Language:English
Subjects:
Algorithms
Image segmentation
Machine learning
Simulation
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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, the employment of such a database is limited or shows suboptimal performance due to the realism gap, missing textures, and the simplified appearance of simulated images. In this work we 1) provide image simulation on virtual XCAT subjects with varying anatomies, and 2) propose sim2real translation network to improve image realism. Our usability experiments suggest that sim2real data exhibits a good potential to augment training data and boost the performance of a segmentation algorithm.
ISSN:2331-8422