Unified and Semantically Grounded Domain Adaptation for Medical Image Segmentation

• URL: http://arxiv.org/abs/2508.08660v2
• Date: Mon, 18 Aug 2025 06:39:33 GMT
• Title: Unified and Semantically Grounded Domain Adaptation for Medical Image Segmentation
• Abstract summary: Most prior unsupervised domain adaptation approaches for medical image segmentation are narrowly tailored to the source-accessible setting.<n>We introduce a unified, semantically grounded framework that supports both source-accessible and source-free adaptation.<n>Our framework achieves state-of-the-art results in both settings, with source-free performance closely approaching its source-accessible counterpart.
• Score: 26.940252595979892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most prior unsupervised domain adaptation approaches for medical image segmentation are narrowly tailored to either the source-accessible setting, where adaptation is guided by source-target alignment, or the source-free setting, which typically resorts to implicit supervision mechanisms such as pseudo-labeling and model distillation. This substantial divergence in methodological designs between the two settings reveals an inherent flaw: the lack of an explicit, structured construction of anatomical knowledge that naturally generalizes across domains and settings. To bridge this longstanding divide, we introduce a unified, semantically grounded framework that supports both source-accessible and source-free adaptation. Fundamentally distinct from all prior works, our framework's adaptability emerges naturally as a direct consequence of the model architecture, without the need for any handcrafted adaptation strategies. Specifically, our model learns a domain-agnostic probabilistic manifold as a global space of anatomical regularities, mirroring how humans establish visual understanding. Thus, the structural content in each image can be interpreted as a canonical anatomy retrieved from the manifold and a spatial transformation capturing individual-specific geometry. This disentangled, interpretable formulation enables semantically meaningful prediction with intrinsic adaptability. Extensive experiments on challenging cardiac and abdominal datasets show that our framework achieves state-of-the-art results in both settings, with source-free performance closely approaching its source-accessible counterpart, a level of consistency rarely observed in prior works. Beyond quantitative improvement, we demonstrate strong interpretability of the proposed framework via manifold traversal for smooth shape manipulation.

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