Low-field MRI offers a portable, cost-effective alternative to conventional high-field scanners but suffers from reduced signal-to-noise ratio and spatial inhomogeneity, which compromise the accuracy and consistency of automated brain structure segmentation. In this work, we introduce atlas-augmented deep learning models that integrate probabilistic anatomical priors to enhance the delineation of pediatric hippocampus and basal ganglia in ultra-low-field MRI (0.064 T). We evaluate seven pipelines on the LISA 2025 dataset (79 T2-weighted scans): baseline VNet, nnU-Net, and MedSAM2 variants (2D and 3D decoders), as well as atlas-augmented VNet, atlas-augmented nnU-Net, and atlas-augmented MedSAM2-3D. For VNet and MedSAM2-3D, probabilistic maps from the Pauli and Harvard-Oxford atlases are encoded and fused with intermediate feature maps, while nnU-Net ingests priors as additional input channels. Baseline nnU-Net attains mean DSCs of 0.71 for hippocampus and 0.86 for basal ganglia; atlas augmentation yields modest hippocampal gains (HD95 \(\downarrow \) 0.05, ASSD \(\downarrow \) 0.06) and more pronounced improvements in basal ganglia segmentation, reflecting richer prior information for larger structures. VNet and MedSAM2 variants exhibit limited hippocampal benefit, highlighting the strength of nnU-Net’s adaptive framework. Our findings establish atlas-augmented nnU-Net as a new benchmark for robust segmentation in resource-constrained, low-field imaging environments. The code for our methods will be publicly accessible after the successful publication of the paper here: https://github.com/mackostya/deepatlas-ulf-seg .