# Adversarial Data Augmentation on Breast MRI Segmentation
## 5 Conclusions  
The availability of annotated datasets is a transversal problem in data science, in particular when concerning medical related exams. Some works have already tried to tackle this issue by approaching domain transfer tasks, employing generative adversarial networks. This work presents an application of some of these GANs to obtain synthetic MRI from label maps so to provide an augmentation dataset for segmentation 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. We found that Pix2 PixHD and CycleGAN show promising results on replicating the complexities of this MRI data, even with several degrees of freedom still remaining on the input label maps. We found that the cycle-consistency nature of the CycleGAN, although a useful setup, is not able to improve certain networks, such as the original U-Net based Pix2 Pix. We also observed that the high frequency detail was not determinant for the successful segmentation of the label maps available and that Pix2 PixHD and CycleGAN synthesis managed to maintain the discrimination capability of the U-Net, even after training was performed without the original set. The contributions made in this work are of practical nature. We aim to more precisely expand our theoretical understanding in the future. For future work we intend on introduce small manipulations of the existing annotation label maps to obtain different pairs and enhance the diversity of the augmented dataset, also leveraging a larger synthetic portion without removing the original data. An additional path to follow involves the annotation and use of more detailed label maps, with finer labels to assist to control the generation such as fibro-glandular tissue, vascular networks, the heart, ribs or intercostal muscles. After this, we plan on further training the segmentation network only on synthsets with larger sizes and evaluate its performance.  
Conceptualization, J.F.T., M.D., L.F.T. and H.P.O.; methodology, J.F.T.; software, J.F.T.; validation, J.F.T.; formal analysis, J.F.T., M.D.; investigation, J.F.T.; resources, E.B., J.C., L.F.T. and H.P.O.; data curation, J.F.T., M.D., E.B. and J.C.; writing--original draft preparation, J.F.T.; writing--review and editing, J.F.T., L.F.T. and H.P.O.; visualization, J.F.T.; supervision, J.F.T., L.F.T. and H.P.O.; project administration, J.F.T. and H.P.O.; funding acquisition, J.F.T. and H.P.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fundacao para a Ciencia e a Tecnologia (FCT) grant within PhD grant number SFRH/BD/135834/2018.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Champalimaud Clinical Centre, Champalimaud Foundation, protocol code BCCT.plan, 7 March 2017.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
* Computed Tomography
* Generative Adversarial Network
* Frechet Inception Distance
* Magnetic Resonance Imaging
* Structure Similarity Index Measure  
Figure 12: Further best visual test set generation results and respective SSIM. ResGAN and Pix2 Pix-HD with BS8. CycleGAN with BS1 and Experience Replay. Part 2.  
Figure 13: Further best visual test results for the segmentation U-Net, trained with the respective synthesis ResGAN and Pix2 PixHD with BS8. CycleGAN with BS1 and Experience Replay. Part 1.