FA-SedLoRA: scene-driven fine-tuning and style disentanglement for domain-generalized cardiac MRI segmentation.
Authors
Affiliations (2)
Affiliations (2)
- Department of Cardiology, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, P.R. China. [email protected].
- Department of Cardiology, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, P.R. China.
Abstract
The advent of deep learning has significantly advanced the state of the art in cardiac magnetic resonance (CMR) image segmentation. However, most models remain task-specific, which hinders the development and validation of approaches generalizable across different clinical centers, imaging protocols, or scanner vendors. Vision foundation models (VFMs), pre-trained on large-scale natural image datasets, offer powerful and transferable representations under the "pre-training and fine-tuning" paradigm. Nevertheless, adapting them to CMR segmentation faces two major challenges: (1) the substantial domain gap between natural and medical images limits transferability; and (2) extracting domain-agnostic features from diverse domain styles represents a key bottleneck for domain generalization with VFMs. To address these issues, we propose a scenario-activated fine-tuning initialization strategy that adapts VFMs to MRI characteristics and employs singular value decomposition to extract principal components for parameter-efficient tuning. This approach enables robust domain-generalized CMR segmentation. Additionally, we apply Haar wavelet transforms to disentangle style information from domain-invariant content. The former helps stabilize scene content, while the latter captures scene style and mitigates its impact on domain-generalized semantic segmentation. Experiments under various CMR domain generalization segmentation settings demonstrate the state-of-the-art performance of our FA-SedLoRA framework and its versatility across different VFMs.