Adversarial Data Augmentation on Breast MRI Segmentation

The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images,...

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Bibliographic Details
Published in:Applied sciences 2021-05, Vol.11 (10), p.4554
Main Authors: Teixeira, João F., Dias, Mariana, Batista, Eva, Costa, Joana, Teixeira, Luís F., Oliveira, Hélder P.
Format: Article
Language:English
Subjects:
Annotations
Brain research
Breast
Data augmentation
Datasets
generation
Image analysis
Image annotation
Image processing
label map
Magnetic resonance imaging
Medical imaging
Medical research
MRI
semantic segmentation
Synthetic data
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Summary:The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator’s architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11104554