When do anatomical priors help? Missing-modality robustness in small-sample dual-modal brain MRI region segmentation

Abstract

Anatomical priors are often assumed to improve medical image segmentation, but their role can be ambiguous when strong image evidence is available. We study this question in a controlled small-sample dual-modal brain MRI setting with 13-class coarse brain-region segmentation from T1 and FLAIR images. Using a quality-controlled 78-case subset and a subject-disjoint five-fold protocol, we compare compact U-Net variants, coordinate channels, modality dropout, fold-wise index-space atlas conditioning, and a full-to-missing modality consistency variant, FW-AtlasMC. Fold-wise probabilistic spatial priors are built only from training labels, and an atlas-only baseline quantifies spatial prior strength without model training. Under full T1+FLAIR input, learned methods are tightly clustered in Dice, and atlas-informed models do not significantly improve full-modality Dice over a 2.5D U-Net baseline. However, among learned image-conditioned models under missing-modality inference, FW-AtlasMC improves FLAIR-only Dice and reduces average performance drop compared with 2.5D U-Net, modality dropout, and FW-Atlas. FW-AtlasMC combines atlas-informed inputs with missing-modality training and full-to-missing consistency. Validation-only sensitivity analysis further indicates that the chosen consistency weight represents a reasonable trade-off within the tested range. These results suggest that anatomical priors are better understood as structural stabilizers under degraded modality information, rather than as universal full-modality accuracy boosters.