Accurate segmentation of the ventricles and myocardium on Cardiac Magnetic Resonance (CMR) images is crucial to assess the functioning of the heart or to diagnose patients suffering from myocardial infarction. However, the domain shift existing between the multiple sequences of CMR data prevents a deep learning model trained on a specific contrast to be used on a different sequence. Domain adaptation can address this issue by alleviating the domain shift between different CMR contrasts, such as Balanced Steady-State Free Precession (bSSFP) and Late Gadolinium Enhancement (LGE) sequences. The aim of the degree project “Domain Adaptation for Multi-Contrast Image Segmentation in Cardiac Magnetic Resonance Imaging” is to apply domain adaptation to perform unsupervised segmentation of cardiac structures on LGE sequences. A style-transfer model based on generative adversarial networks is trained to achieve modality-to-modality translation between LGE and bSSFP contrasts. Then, a supervised segmentation model is developed to segment the myocardium, left and right ventricles on bSSFP data. Final segmentation is performed on synthetic bSSFP obtained by translating LGE images. Our method shows a significant increase in Dice score compared to direct segmentation of LGE data. In conclusion, the results demonstrate that using domain adaptation based on information from complementary CMR sequences is a successful approach to unsupervised segmentation of Late Gadolinium Enhancement images.