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arXiv · 2509.17187

Ambiguous Medical Image Segmentation Using Diffusion Schr\"{o}dinger Bridge

Lalith Bharadwaj Baru·Kamalaker Dadi·Tapabrata Chakraborti·Raju S. Bapi
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Abstract

Accurate segmentation of medical images is challenging due to unclear lesion boundaries and mask variability. We introduce \emph{Segmentation Sch\"{o}dinger Bridge (SSB)}, the first application of Sch\"{o}dinger 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.

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Lalith Bharadwaj Baru, Kamalaker Dadi, Tapabrata Chakraborti, Raju S. Bapi. 2025-09-21. Ambiguous Medical Image Segmentation Using Diffusion Schr\"{o}dinger Bridge. https://arxiv.org/abs/2509.17187

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