Convolutional neural networks (CNNs) have achieved great success in automating the segmentation of medical images. Nevertheless, when a trained CNN is tested on a new domain there is a performance degradation due to the distribution shift. In this work, we present an unsupervised Extensive Pixel-level Augmentation framework (EPA) for cross-modality domain adaptation. EPA implements a two-phase image- and feature-level adaptation method. In the first phase, the source domain images are mapped to target domain in pixel space using the CycleGAN, StAC-DA, and CUT translation models. This creates an augmented translated dataset 3 times bigger than the original. In phase 2, a deeply supervised U-Net network is trained to segment the target images using a semi-supervised adversarial learning approach. In particular, a set of discriminator networks are trained to distinguish between the target and source domain segmentations, while the U-Net aims to fool them. EPA is tested on the task of brain structure segmentation from the Crossmoda 2022 Grand Challenge, being ranked within the top 12 submissions of the testing phase. Moreover, we demonstrate that augmenting the size of the mapped dataset through distinct translation methods is crucial for increasing the segmentation accuracy of the model.