On the effectiveness of GAN generated cardiac MRIs for segmentation
- URL: http://arxiv.org/abs/2005.09026v2
- Date: Fri, 22 May 2020 09:28:39 GMT
- Title: On the effectiveness of GAN generated cardiac MRIs for segmentation
- Authors: Youssef Skandarani, Nathan Painchaud, Pierre-Marc Jodoin, Alain
Lalande
- Abstract summary: We propose 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" modules to generate realistic MR images tailored to a given anatomical map.
We show that segmentation with CNNs trained with our synthetic annotated images gets competitive results compared to traditional techniques.
- Score: 12.59275199633534
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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