Ambiguous Medical Image Segmentation Using Diffusion Schrödinger Bridge
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911167981551616 |
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| author | Baru, Lalith Bharadwaj Dadi, Kamalaker Chakraborti, Tapabrata Bapi, Raju S. |
| author_facet | Baru, Lalith Bharadwaj Dadi, Kamalaker Chakraborti, Tapabrata Bapi, Raju S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ambiguous Medical Image Segmentation Using Diffusion Schrödinger Bridge Baru, Lalith Bharadwaj Dadi, Kamalaker Chakraborti, Tapabrata Bapi, Raju S. Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Ambiguous Medical Image Segmentation Using Diffusion Schrödinger Bridge |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.17187 |