The segmentation of low-field pediatric brain MR images is an important topic, as it can show the development of the pediatric brain. At the same time, the low cost and maintenance required by the low-field scanners make the technology more accessible in wider parts of the world compared to high-field MRI-scanners. The wider accessibility allows to understand the pediatric brain development in different regions of the world, thereby allowing to better understand the effects of, for example, nutrition for the brain development. Despite these advantages, automatically segmenting low-field MRI scans can be challenging: The internal brain structure can be hard to recognize in the low-field MRI images, the ground-truth segmentation can be unprecise due to registration errors between high-field and low-field MRI images, and the segmentation can potentially be different for the hippocampus region depending on whether it is the left or right hippocampus. In order to still be able to achieve the best possible segmentation scores, we try to increase the probability that our predicted segmentation is close to the ground-truth segmentation. To achieve this, we focus on extending the data-augmentation and make use of an ensemble of networks, of which we take the average as our final prediction. Doing so led to high-scores for the task 2 of the LISA challenge. The code is available at https://github.com/NikolasMorshuis/nnUNet-LISA-Challenge.git .