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

Equivocal 3D lesion segmentation is plagued by high inter-observer variability, which conventional deterministic models fail to account for, resulting in over-confident masks that overlook clinical risks. To address this, researchers have proposed Volumetric Directional Diffusion, a method that anchors uncertainty quantification in anatomical consensus. This approach aims to improve the accuracy of ambiguous medical image segmentation by capturing aleatoric uncertainty. Unlike standard diffusion methods, which can lead to structural fractures and out-of-distribution samples, Volumetric Directional Diffusion recovers complex topology while preserving anatomical consistency¹. By acknowledging and quantifying uncertainty, this method can provide more reliable segmentation masks, ultimately reducing clinical risks. This matters to medical imaging practitioners because it can lead to more accurate diagnoses and treatments, highlighting the importance of uncertainty quantification in medical image analysis.