Adversarial Data Augmentation on Breast MRI Segmentation

João F Teixeira,Mariana Dias,Joana Costa,Luís F Teixeira,Eva Batista,Hélder P Oliveira

doi:10.3390/app11104554

Abstract

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.

Highlights

Coping with small and poorly annotated datasets has been a recurring problem for medical image analysis researchers, limiting their success in validating supervised learning algorithms for real-life use
We explore how some generative adversarial networks can be used for the synthesis of breast magnetic resonance images from corresponding annotations, for data augmentation purposes
We study some particularities of these generative architectures, their outcomes and the effects of these synthetic sets on the learning of a U-Net semantic segmentation network

Summary

Introduction

Coping with small and poorly annotated datasets has been a recurring problem for medical image analysis researchers, limiting their success in validating supervised learning algorithms for real-life use. The paucity of comprehensive and annotated medical data is due to several factors: acquiring medical images often involves expensive and invasive procedures, and annotating them is a time-consuming task that requires the labor of experienced specialists. These datasets are often unbalanced and lack variability since abnormal exams are captured less frequently than normal ones, contributing to unsatisfying performances in classification tasks. Images produced by traditional data augmentation techniques (e.g., rotation, translation, crop, shear) are often highly correlated with the already available ones, which makes this strategy insufficient to counteract the consequences of data scarcity. One of the most promising approaches that emerged from that research is Generative Adversarial Networks (GANs) [1], which was already shown to be successful for the synthesis of natural images [2] and for super-resolution tasks [3]

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