Ultra-low-field (uLF) MRI is a promising technology for expanding access to MRI in resource-limited settings, but it presents significant image analysis challenges, particularly in the segmentation of small brain structures. One such structure is the hippocampus, which is crucial for monitoring the neurodevelopment after birth. In this study, we developed a hippocampal segmentation method using the nnUNet framework, exploring various training strategies, including using additional labeled high-field (HF) and unlabeled low-field (LF) data. Our results show that integrating external datasets improves segmentation accuracy over using uLF data alone, even with substantial differences in imaging parameters and field strengths. This approach highlights the importance of leveraging diverse datasets to enhance the performance of segmentation models in low-quality imaging modalities, potentially leading to better diagnostic capabilities in challenging clinical environments.