DiffuPrompt: Adapting Video Foundation Models to 3D Medical Volumes via Latent Trajectory Priors

Adapting video foundation models to volumetric medicalsegmentation by treating axial depth as a temporal dimension is attrac-tive, but the resulting slice propagation can be unstable when anatom-ical structures branch, disappear, or become poorly contrasted acrossslices. Static geometric prompts provide little guidance for newly ap-pearing or disconnected components, and SAM2-style streaming mem-ory may accumulate errors in these regions. We propose Dix001BuPrompt,a backbone-frozen, single-prompt framework that augments SAM2 withlatent trajectory priors for volumetric propagation. Given a user prompton a representative slice, an LLM-conditioned Neural ODE predicts acontinuous, patient-conditioned latent trajectory and converts it into apersistent prior memory. A dual-stream spatial gate then fuses this priormemory with the standard observation-driven working memory, allowingthe model to use image evidence when it is reliable and trajectory pri-ors when local propagation becomes ambiguous. Experiments on MSD,BTCV, and BraTS show improved promptable volumetric segmentationover SAM/SAM2-based baselines, especially on boundary-sensitive met-rics.