Ambiguous Medical Image Segmentation Using Diffusion Schrödinger Bridge
It addresses segmentation challenges in medical imaging for clinicians and researchers, with incremental improvements in modeling and evaluation.
The paper tackles ambiguous medical image segmentation by introducing Segmentation Schrödinger Bridge (SSB), which models joint image-mask dynamics to handle unclear lesion boundaries and mask variability, achieving state-of-the-art performance on datasets like LIDC-IDRI, COCA, and RACER.
Accurate segmentation of medical images is challenging due to unclear lesion boundaries and mask variability. We introduce \emph{Segmentation Schödinger Bridge (SSB)}, the first application of Schödinger Bridge for ambiguous medical image segmentation, modelling joint image-mask dynamics to enhance performance. SSB preserves structural integrity, delineates unclear boundaries without additional guidance, and maintains diversity using a novel loss function. We further propose the \emph{Diversity Divergence Index} ($D_{DDI}$) to quantify inter-rater variability, capturing both diversity and consensus. SSB achieves state-of-the-art performance on LIDC-IDRI, COCA, and RACER (in-house) datasets.