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

2026-08-23

AI Summary

本文提出SymmAdapt方法，通过双向流匹配生成伪标签和合成图像解决医学图像分割中的无源域适应问题。

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.

Problem

• Domain Shift
• Medical Image Segmentation
• Source-Free Unsupervised Domain Adaptation
• Imaging Modalities
• Acquisition Sites

Innovation

• Symmetrical Flow Matching
• Source-free Unsupervised Domain Adaptation (SFUDA)
• Generative Replay Buffer
• Domain-agnostic Gaussian Origin
• Structural Consistency