TCSA-UDA: Text-Driven Cross-Semantic Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation
This work addresses the problem of domain shifts in medical imaging for researchers and practitioners, offering a novel integration of language-driven semantics, though it is incremental in building on vision-language methods for a specific application.
The paper tackles the challenge of unsupervised domain adaptation for medical image segmentation across different imaging modalities like CT and MRI by proposing TCSA-UDA, a framework that uses text-driven cross-semantic alignment to guide visual representation learning, resulting in significant reduction of domain shift and consistent outperformance of state-of-the-art methods on benchmarks such as cardiac, abdominal, and brain tumor segmentation.
Unsupervised domain adaptation for medical image segmentation remains a significant challenge due to substantial domain shifts across imaging modalities, such as CT and MRI. While recent vision-language representation learning methods have shown promise, their potential in UDA segmentation tasks remains underexplored. To address this gap, we propose TCSA-UDA, a Text-driven Cross-Semantic Alignment framework that leverages domain-invariant textual class descriptions to guide visual representation learning. Our approach introduces a vision-language covariance cosine loss to directly align image encoder features with inter-class textual semantic relations, encouraging semantically meaningful and modality-invariant feature representations. Additionally, we incorporate a prototype alignment module that aligns class-wise pixel-level feature distributions across domains using high-level semantic prototypes. This mitigates residual category-level discrepancies and enhances cross-modal consistency. Extensive experiments on challenging cross-modality cardiac, abdominal, and brain tumor segmentation benchmarks demonstrate that our TCSA-UDA framework significantly reduces domain shift and consistently outperforms state-of-the-art UDA methods, establishing a new paradigm for integrating language-driven semantics into domain-adaptive medical image analysis.