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

arXiv: https://arxiv.org/abs/2208.04874 · HTML: https://mlsanity.com/paper/2208.04874/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Deep CT to MR Synthesis using Paired and Unpaired Data](https://mlsanity.com/paper/1805.10790/) (2018)
- [Generation of annotated multimodal ground truth datasets for abdominal medical image registration](https://mlsanity.com/paper/2012.01582/) (2020)

## Built on by (most cited first, 1 of 1)

- [Translating Simulation Images to X-ray Images via Multi-Scale Semantic Matching](https://mlsanity.com/paper/2304.07693/) (2023)

## Abstract (arXiv)

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.