Magnetic Resonance Imaging (MRI) provides a non-invasive means to examine pediatric brain anatomy. However, in low-resource settings, ultra-low-field scanners are more widely used due to their affordability and portability, but they often generate images with a low signal-to-noise ratio and poor tissue contrast. This severely hampers the accurate delineation of critical subcortical structures such as the basal ganglia. In this work, we adapt and refine the multi-scale Transformer – CNN network (MS-TCNet) for bilateral basal ganglia segmentation in 0.064T pediatric MRI. Our optimized version, OMS-TCNet, integrates improved data augmentation and fine-tuned training configurations. Extensive experiments on the challenge dataset demonstrate that our method achieves robust and reliable segmentation performance under ultra-low-field imaging conditions. The code is publicly available at: https://github.com/Onion-Boy/OMS-TCNet .