Medical image deep learning segmentation has shown great potential in becoming an ubiquitous part of the clinical analysis pipeline. However, these methods cannot guarantee high quality predictions when the source and target domains are dissimilar due to different acquisition protocols, or biases in patient cohorts. Recently, unsupervised domain adaptation techniques have shown great potential in alleviating this problem by minimizing the shift between the source and target distributions. In this work, we aim to predict tissue segmentation maps on an unseen dataset, which has both different acquisition parameters and population bias when compared to our training data. We achieve this by investigating two unsupervised domain adaptation (UDA) techniques with the objective of finding the best solution for our problem. We compare the two methods with a baseline fully-supervised segmentation network in terms of cortical thickness measures.