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

Autor:	Amirrajab, Sina, Khalil, Yasmina Al, Lorenz, Cristian, Weese, Jurgen, Pluim, Josien, Breeuwer, Marcel
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting 07-12 May 2022 | London, England, UK
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.04874 Zobrazit plný text záznamu View this record from Arxiv