Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation

Researchers propose Volumetric Directional Diffusion (VDD), a new AI method for medical image segmentation that addresses uncertainty in 3D lesion analysis. VDD anchors generative models to consensus priors to maintain anatomical accuracy while capturing expert disagreements, achieving state-of-the-art uncertainty quantification on multiple medical datasets.

• →VDD solves the fidelity-diversity trade-off in medical image segmentation by anchoring diffusion models to deterministic consensus priors.
• →The method prevents anatomical hallucinations and structural fractures common in standard generative approaches.
• →Validation on three datasets (LIDC-IDRI, KiTS21, ISBI 2015) shows significant improvements in uncertainty quantification metrics.
• →VDD provides clinically useful uncertainty maps for safer medical decision-making in radiotherapy and surgery.
• →The approach maintains competitive segmentation accuracy while addressing inter-observer variability in medical imaging.