SymmAdapt：用于医学图像分割的无监督源域自适应的对称流匹配方法
SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation
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中文总结 AI 辅助
针对医学图像分割的域偏移问题，提出基于对称流匹配的SFUDA框架，构建生成式回放缓冲区，在多任务评估中优于基线且与传统UDA方法性能相当。
中文摘要 AI 辅助
成像模态与采集站点间的域偏移仍是阻碍分割模型临床部署的重要障碍。无监督源域自适应（SFUDA）可在无需访问敏感源数据的情况下，将预训练模型适配至无标注目标域，以此解决该问题。我们提出一种基于对称流匹配（Symmetrical Flow Matching）的新型SFUDA框架，该统一生成模型可在同一学习流中对输入图像进行分割，并从掩码合成类源图像。通过从与域无关的高斯原点初始化推理，模型可保持跨域结构一致性，使预测基于学习到的解剖结构而非偏移的纹理统计。我们的管线利用该对称性，从无标注目标数据生成可靠伪标签及对应类源合成图像，构建生成式回放缓冲区，在对真实目标图像与合成类源图像的联合集进行微调的生成式自训练阶段锚定源域知识。我们在腹部多器官、心脏分割（涵盖跨模态MRI与CT偏移）及多站点前列腺分割任务上进行评估，结果表明，我们的方法优于SFUDA基线方法，且与传统UDA方法性能相当。
英文摘要
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.
发表机构
- Faculty of Engineering, Tel Aviv University（特拉维夫大学工程学院）
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