From Minimal Clinical Prompts to 3D: Spacing-Aware Prompt Propagation for Multimodal Prostate Lesion Segmentation in bpMRI

Accurate 3D delineation of prostate cancer lesions in biparametric MRI (bpMRI) supports targeted biopsy and therapy planning, yet clinical reports typically provide only sparse 2D measurements or a single contour. We present SAPP (Spacing-Aware Prompt Propagation), a prompt-to-volume adaptation of SAM 2 that converts one clinical prompt on a single slice (diameter, box, circle, or contour) into a coherent 3D lesion mask from multimodal bpMRI. SAPP couples adaptive multimodal fusion for accurate image-level prompted segmentation with a spacing-aware volume-level prompt propagation module. The propagation uses spacing-decayed, confidence-gated streaming-memory attention and a learned stop rule to prevent leakage in anisotropic volumes. Trained on 3,256 scans from 7 institutions, SAPP generalizes to 14 held-out cohorts (3,504 fully annotated scans plus 516 routine-care weak-label studies), achieving 0.84/0.81 DSC on the prompted slice (box/diameter) and 0.86/0.78/0.76 DSC for volumetric masks (oracle-mask/box/diameter), consistently outperforming baselines while reducing over-propagation. Beyond segmentation, SAPP can accelerate annotation workflows and enable scalable generation of high-quality volumetric lesion masks, supporting the development of robust clinical AI models.