Diffusion-Driven Temporal Prototypes for Longitudinal Glioma Evolution Segmentation

Monitoring glioma progression using longitudinal MRI is hindered by inconsistent follow-up intervals, variations in imaging quality, and non-linear morphological changes. DiffProto-Net addresses these challenges by learning temporal lesion prototypes refined through a diffusion-based reconstruction process. The network stabilizes temporal representations by encouraging smooth evolution patterns and reducing noise-induced variability. It also models lesion progression trajectories, producing temporally coherent segmentation masks across all recorded timepoints. Experiments conducted on 830 longitudinal MRI sequences (3–6 timepoints per patient) show that DiffProto-Net achieves a TC-Dice of 0.874, outperforming UNet-LSTM (0.785, +11.3%) and ST-UNet (0.812, +7.6%). Temporal volume deviation decreases from 14.2% to 6.8%, indicating superior stability in tracking tumor evolution. For progression prediction, the model achieves an AUC of 0.903, representing a 12.5% improvement over 3D-ResNet. Under synthetic timing perturbations, temporal drift is reduced by 15.1%, and ablation experiments reveal that diffusion-based prototype refinement contributes nearly 10% Dice improvement.