# GAN-enhanced Conditional Echocardiogram Generation

arXiv: https://arxiv.org/abs/1911.02121 · HTML: https://mlsanity.com/paper/1911.02121/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Transesophageal Echocardiography Generation using Anatomical Models](https://mlsanity.com/paper/2410.06781/) (2024)
- [Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2205.09842/) (2022)

## Abstract (arXiv)

Echocardiography (echo) is a common means of evaluating cardiac conditions. Due to the label scarcity, semi-supervised paradigms in automated echo analysis are getting traction. One of the most sought-after problems in echo is the segmentation of cardiac structures (e.g. chambers). Accordingly, we propose an echocardiogram generation approach using generative adversarial networks with a conditional patch-based discriminator. In this work, we validate the feasibility of GAN-enhanced echo generation with different conditions (segmentation masks), namely, the left ventricle, ventricular myocardium, and atrium. Results show that the proposed adversarial algorithm can generate high-quality echo frames whose cardiac structures match the given segmentation masks. This method is expected to facilitate the training of other machine learning models in a semi-supervised fashion as suggested in similar researches.