Summary of Generative Adversarial Networks in Ultrasound Imaging: Extending Field Of View Beyond Conventional Limits, by Matej Gazda et al.
Generative Adversarial Networks in Ultrasound Imaging: Extending Field of View Beyond Conventional Limits
by Matej Gazda, Samuel Kadoury, Jakub Gazda, Peter Drotar
First submitted to arxiv on: 31 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel application of conditional Generative Adversarial Networks (cGANs) to extend the field of view (FoV) in Transthoracic Echocardiography (TTE) ultrasound imaging while maintaining high resolution. The proposed cGAN architecture, echoGAN, demonstrates outpainting capabilities, effectively broadening the viewable area in medical imaging. This advancement has the potential to enhance both automatic and manual ultrasound navigation, offering a more comprehensive view that could significantly reduce the learning curve associated with ultrasound imaging and aid in more accurate diagnoses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computer technology to improve heart scans. Right now, heart scans have a limited view of what’s inside the heart, which can make it hard for doctors to diagnose problems. The new technique uses something called Generative Adversarial Networks (GANs) to make the scan area bigger and clearer. This could help doctors learn how to use heart scans more easily and get better results. |
