## 1 Introduction
[7], they initialized a geometrical template from DE MRI and then deformed it to match the image data, using some prior knowledge regarding shape, contours, and region intensity distribution. Afterwards, they built a 3D mesh from Cine MRI and registered it to DE MRI space. Finally, they deformed this mesh towards the 2D contours obtained in the first step, resulting in a mesh representing the myocardium in 3D. In the work of [PERSON] et al. [8], they performed DE MRI segmentation using pre-trained convolutional neural networks on a Cine MRI dataset. Although there are several ways to use information from different modalities to segment a target, data fusion is one of the most common approaches nowadays. A review of deep learning segmentation architectures with different fusion strategies was presented by [PERSON] et al. [9]. They divided them into input-level fusion, layer-level fusion, and decision-level fusion. In the first one, the modalities are combined channel by channel and then fed to the segmentation network. In the second one, each modality is used as input for separate segmentation networks, and the individual features are fused within the layers of the networks. In the third one, each modality is used to train separate segmentation networks, and the output is then combined to obtain the final segmentation result. This schematic can also be thought of as output fusion. In [PERSON] et al. [10], several UNet-based fusion architectures were compared in terms of performance for left ventricle segmentation from multi-modal MRI. They found out that an intermediate fusion UNet-based architecture (DualUNet) proved to work best when segmenting both the left ventricle cavity and the myocardium. This architecture is inspired by the work of [PERSON] et al. [11], where T1 and T1 flipped brain scan images are fused before the decoding path for stroke lesion segmentation. Although deep-learning-based segmentation methods have shown promising results in a variety of different applications, they do need big amounts of data in order to generalize well. This is quite problematic in the field of medical imaging, since the amount of available data is generally limited, and the annotation task is complex and costly. Data augmentation emerged as a technique to solve the above-mentioned problem. As its name suggests, it involves generating new data samples by applying several transformations to the existing one. Some of the most common transformations reported in the literature include rotations, translations, scaling, and deformations. Additionally, there is a group of deep learning methods, commonly known as generative models, which can automatically create synthetic images, and thus have also been used for data augmentation. There have been some previous works where data augmentation has been used for segmentation in cardiac MRI. For instance, [PERSON] et al. [12] proposed an adversarial data augmentation pipeline that can dynamically optimize its image transformation parameters to produce results that are realistic and plausible in typical medical imaging setups. [PERSON] et al. [13] proposed a shape-based data augmentation algorithm where they learn different representations of the left ventricle and capture structural relationships between shapes. Given a dataset of images with their corresponding left ventricle contours, one image is selected as a reference, and the contours of the rest are deformed to match the reference one. The transformation parameters are stored and taken as shape features. New shapes are generated from an orthonormal feature basis that is computed based on eigenvector analysis on the correlation matrix of shape features. [PERSON] et al. [14] proposed the use of variational autoencoders and generative adversarial networks (GANs) [15] for generating realistic synthetic MRI. Their results show that training convolutional neural networks with the data generated by their model achieved competitive performance with respect to other traditional techniques.