Summary of Synthetic Data Generation For 3d Myocardium Deformation Analysis, by Shahar Zuler and Dan Raviv
Synthetic Data Generation for 3D Myocardium Deformation Analysis
by Shahar Zuler, Dan Raviv
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach generates synthetic CT datasets with ground truth annotations to address the scarcity of 3D myocardium deformation datasets. This enables the development of robust myocardium deformation analysis models. The method uses a combination of CT scans and cardiac motion simulations to create realistic synthetic datasets, which can be used to train and evaluate myocardium deformation analysis models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to create fake CT scans that have the same information as real ones, but are made just for training computer models. This is helpful because there aren’t many real CT scans with details about how the heart moves. The method combines real CT scans and simulations of heart movement to make new, realistic datasets. |
