Accurate segmentation of subcortical brain structures in MRI is essential for the study of neurodevelopment, particularly in pediatric populations. While low-field MRI scanners offer a cost-effective and safer alternative to high-field systems—especially eliminating the need for sedation in young children—they present challenges due to lower image resolution and signal-to-noise ratio. In this work, we propose a symmetry-aware post-processing strategy to improve the segmentation of bilateral structures in low-field MRI. We first train baseline U-Net models for the segmentation of eight anatomical structures, including hippocampi, in the LISA 2025 pediatric low-field MRI dataset. While these models achieve reasonable accuracy, we observe frequent violations of anatomical symmetry in their predictions. To address this, we introduce a novel correction step that explicitly enforces plausible anatomical symmetry by identifying discrepancies between hemispheres and applying deformation fields anchored by the dominant structure from each symmetric pair. This post-hoc alignment improves segmentation quality for all symmetric targets, particularly the hippocampi. Our approach highlights the importance of leveraging anatomical priors in low-resource imaging scenarios and paves the way for more reliable analyses in global health contexts(Code: https://github.com/Zrrr1997/LISA_2025_cvhci).
