SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation

• URL: http://arxiv.org/abs/2608.22532v1
• Date: Sun, 23 Aug 2026 18:09:39 GMT
• Title: SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation
• Abstract summary: Source-free unsupervised domain adaptation (SFUDA) adapts a pretrained model to an unlabeled target domain without requiring access to sensitive source data.<n>We introduce a novel SFUDA framework built on Symmetrical Flow Matching.<n>We evaluate on abdominal multi-organ and cardiac segmentation, covering cross-modality MRI->CT shifts, and multi-site prostate segmentation.
• Score: 0.8717253904965373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-free unsupervised domain adaptation (SFUDA) addresses this by adapting a pretrained model to an unlabeled target domain without requiring access to sensitive source data. We introduce a novel SFUDA framework built on Symmetrical Flow Matching, a unified generative model that segments an input image and synthesizes a source-like image from a mask within the same learned flow. By initializing inference from a domain-agnostic Gaussian origin, the model preserves structural consistency across domains and grounds predictions in learned anatomy rather than shifted texture statistics. Our pipeline leverages this symmetry to generate reliable pseudo-labels and corresponding source-like synthetic images from unlabeled target data, creating a generative replay buffer that anchors source knowledge during a generative self-training stage that fine-tunes on a joint set of real target and synthetic source-like images. We evaluate on abdominal multi-organ and cardiac segmentation, covering cross-modality MRI<->CT shifts, and multi-site prostate segmentation. Our approach outperforms SFUDA baselines and is competitive with conventional UDA methods.

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