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This study introduces AURA, an asymmetric supervision strategy based on nnU-Net for pediatric brain MRI segmentation using ultra-low-field (ULF) MRI data. By treating high-field-derived and low-field-edited annotations as distinct observations, AURA effectively incorporates weakly delineated anatomical boundaries and improves segmentation accuracy. The approach achieved competitive Dice scores and demonstrated the potential for enhanced performance in challenging ULF imaging scenarios, highlighting the importance of tailored annotation strategies in medical imaging.
AURA's innovative approach to leveraging asymmetric annotations leads to segmentation performance that rivals traditional methods, even in the challenging context of ultra-low-field MRI.
Portable ultra-low-field (ULF) MRI can expand access to pediatric neuroimaging, but segmentation at 0.064 T remains challenging because anatomical boundaries are weakly delineated, small structures may be only partially visible, and high-field references can be locally misregistered. The LISA 2026 Challenge provides two non-equivalent annotations reflecting different sources of anatomical evidence: a highfield-derived (HF) mask defining the scored target and a low-field-edited (LF) mask aligned with visible ULF anatomy. In this challenge report, we describe AURA, an nnU-Net-based asymmetric supervision strategy that treats these annotations as distinct observations rather than interchangeable ground truths. AURA anchors training to the HF mask and incorporates the LF mask through a bounded reliability gate based on label disagreement, boundaries, predictive uncertainty, class reliability, and training stage. On a 16-case development split, the HF-supervised baseline, AURA, and their ensemble achieved Dice scores of 0.7984, 0.7950, and 0.7988, respectively, while the ensemble achieved an HD95 of 1.8892 and an ASSD of 0.7855. These results provide a preliminary evaluation of AURA within the LISA 2026 Challenge and motivate further assessment on the hidden test set and external ULF cohorts. Our code and pretrained models are available at https://github.com/minhdang050806/ A-nnU-Net-based-asymmetric-supervision-strategy.
