用对称流模型生成伪标签，实现医疗图像分割的无源域适应。
SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation
- 基于对称流匹配，统一图像分割与源域风格合成。
- 在多模态和多中心数据上显著优于现有无源域适应方法。
- 适合缺乏源数据但需跨域部署医疗分割模型的场景。
不同成像模态和采集站点间的分布偏移仍是医疗图像分割模型临床应用的主要障碍。无源无监督域适应（SFUDA）通过在不访问敏感源数据的情况下，将预训练模型适配到未标注目标域来应对该问题。本文提出一种新型SFUDA框架，基于对称流匹配（Symmetrical Flow Matching），该模型可统一完成图像分割与从掩码生成类源域图像的任务。通过从与域无关的高斯起点初始化推理，模型保持跨域结构一致性，预测基于学习到的解剖结构而非漂移的纹理统计。该流程利用对称性，从无标签目标数据生成可靠的伪标签及对应的类源域合成图像，构建生成回放缓冲区，在生成自训练阶段联合微调真实目标数据与合成源域图像。实验覆盖腹部多器官和心脏分割任务，涵盖MRI<->CT跨模态迁移以及多中心前列腺分割。结果表明，本方法显著优于现有SFUDA基线，且性能可媲美传统有监督域适应方法。
原文摘要 · Abstract (English)
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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