# On the effectiveness of GAN generated cardiac MRIs for segmentation

arXiv: https://arxiv.org/abs/2005.09026 · HTML: https://mlsanity.com/paper/2005.09026/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 6 of 6)

- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [Neuralizer: General Neuroimage Analysis without Re-Training](https://mlsanity.com/paper/2305.02644/) (2023)
- [Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs](https://mlsanity.com/paper/2209.04223/) (2022)
- [CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation](https://mlsanity.com/paper/2309.03320/) (2023)
- [Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?](https://mlsanity.com/paper/2107.11447/) (2021)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)

## Abstract (arXiv)

In this work, we propose a Variational Autoencoder (VAE) - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate groundtruth for the application of cine-MR image cardiac segmentation. On one side of our model is a Variational Autoencoder (VAE) trained to learn the latent representations of cardiac shapes. On the other side is a GAN that uses "SPatially-Adaptive (DE)Normalization" (SPADE) modules to generate realistic MR images tailored to a given anatomical map. At test time, the sampling of the VAE latent space allows to generate an arbitrary large number of cardiac shapes, which are fed to the GAN that subsequently generates MR images whose cardiac structure fits that of the cardiac shapes. In other words, our system can generate a large volume of realistic yet labeled cardiac MR images. We show that segmentation with CNNs trained with our synthetic annotated images gets competitive results compared to traditional techniques. We also show that combining data augmentation with our GAN-generated images lead to an improvement in the Dice score of up to 12 percent while allowing for better generalization capabilities on other datasets.