HippoMamba: A Mamba-augmented network for hippocampal segmentation and serial volumetry in Alzheimers disease

원문 초록

Hippocampal atrophy is among the earliest structural markers of Alzheimer's disease, and reliable hippocampal segmentation across serial MRI visits is a prerequisite for measuring it. We present HippoMamba, a network that combines a 3D convolutional encoder and decoder with a Mamba selective state space block at the bottleneck, designed to process one or more MRI visits of a subject. On the Medical Segmentation Decathlon hippocampus dataset (260 subjects, five-fold cross-validation), HippoMamba obtained a mean Dice similarity coefficient of 0.8861 $\pm$ 0.0094, the highest among six convolutional and transformer-based methods trained with the same folds and patch size, and its per-subject Dice was significantly higher than that of each method (Holm-corrected Wilcoxon tests, $p \leq 0.013$). Its mean 95th percentile Hausdorff distance (1.45 mm) and average surface distance (0.44 mm) were the lowest of the compared methods, although they did not differ significantly from those of Attention U-Net, and the Hausdorff distance did not differ significantly from that of SegResNet. Without retraining, the five-fold ensemble was applied to 600 T1-weighted sessions from 150 participants of the OASIS-3 cohort. In sessions meeting a registration quality criterion, predicted hippocampal volumes ranked consistently with FreeSurfer volumes (Pearson r = 0.778, 95% CI 0.699 to 0.838; ICC(3,1) = 0.772) but were systematically smaller by about 27%. In a pre-specified analysis of participants followed for at least two years, annualised volume change derived from per-visit segmentation was moderately correlated with that of FreeSurfer (r = 0.503, 95% CI 0.319 to 0.659), with a larger mean rate. These results support the use of HippoMamba for hippocampal segmentation and for ranking hippocampal volume within a cohort, with absolute volumes and rates of change interpreted relative to the reference method.