{"ID":2868219,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17187","arxiv_id":"2509.17187","title":"Ambiguous Medical Image Segmentation Using Diffusion Schrödinger Bridge","abstract":"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.","short_abstract":"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 pre...","url_abs":"https://arxiv.org/abs/2509.17187","url_pdf":"https://arxiv.org/pdf/2509.17187v1","authors":"[\"Lalith Bharadwaj Baru\",\"Kamalaker Dadi\",\"Tapabrata Chakraborti\",\"Raju S. Bapi\"]","published":"2025-09-21T18:16:06Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Diffusion Model\"]","has_code":false}