## 1 Introduction  
Cardiovascular disease is the leading cause of death globally. One key metric for assessing cardiac performance is ejection fraction (EF), which is measured using a type of magnetic resonance imaging (MRI) sequence termed _cine_ (i.e., dynamic) imaging. A different MRI sequence termed, tagged MRI, is the clinical reference standard for measuring myocardial strain [1], which can be measured along the radial, circumferential and longitudinal directions (Figures 1a and 2). Myocardial strain has a high prognostic value for diagnosing cardiovascular diseases [2]. For example, pathologies such as concentric hypertrophy and eccentric hypertrophy may exhibit the same EF, but can be distinguished by their radial and circumferential strain measures [3]. Tagged CMR imaging is not currently included in a standard clinical workflow due to time-consuming analysis and the need for specialized software [1]. Recently there has been substantial progress towards automating this processing, such as the development of models that automate strain analysis of tagged CMR image sets [6; 7]. However, the model requires manual input of myocardial reference points by a domain expert, and thus the initialization of reference points is currently a barrier to full automation of this process. In recent years, the most widely used methods in medical image segmentation have been based on DL. Within cardiac MRI, the most common segmentation task is delimiting cardiovascular anatomical structures such as the chambers and the coronary arteries. An example from the Automated Cardiac Diagnosis Challenge (ACDC) is presented in Figure 1b. The ACDC also provided a public dataset of cardiac _cine_ images and annotations. The quantification of these structures allows for the measurement of chamber volumes,  
Figure 1: (**a**) Macro-scale diagram of the reference directions most commonly used in cardiac imaging, adapted from [PERSON] et al. (2022) [4]. (**b**) Annotated SAX CMR slice from the Automated Cardiac Diagnosis Challenge (ACDC) public dataset, MICCAI 2017 [5]. The expert annotations include segmentation masks for the LV blood pool, myocardium, and RV (respectively indicated in red, green, and blue). This particular subject was diagnosed with Dilated Cardiomyopathy (DCM). Figure 2: Example of SAX _cine_ and associated tagged cardiac MR images through time from a control patient. Times \(\{0,12,24\}\) are displayed from recordings of 25 time-frames. myocardial mass, wall thickness, and EF. State-of-the-art models for the segmentation of the myocardium and the ventricular chambers from cardiac _cine_ are typically DL approaches using Unet and ResNet architectures [8; 9]. Herein, we adapt these types of architectures for the development of a myocardial segmentation tool for tagged MRI. The state-of-the-art _cine_ segmentation models are able to achieve a dice coefficient of 0.849 [10]. This provides a performance reference for the current work. However, the training of a segmentation network for cardiac tagged images requires a large data set of tagged images. Currently, there is a paucity of publicly available tagged CMR data sets with myocardial annotations. We have identified this as a key obstacle to the development of a tagged segmentation model. However, there is a relative abundance of publicly available cardiac time images with annotations. Herein, we make use of this data for training the tagged segmentation model by transforming the _cine_ images into pseudo-tagged images. The transformation of cine to pseudo-tagged images can be framed as a style transfer problem. Within the DL literature, there has been recent development of models that are able to accurately learn an image \"style\" and then apply a transformation to an image of a different style. Cyclic Generative Adversarial Networks are neural network architectures that are well suited to this problem [11]. In recent years, GAN-based models have gained a lot of traction within the field of medical image computing due to their conditional image generation ability.