Accurate liver segmentation in multi-phase MRI is vital for liver fibrosis assessment, yet labeled data is often scarce and unevenly distributed across imaging modalities and vendor systems. We propose a label-efficient segmentation approach that promotes cross-modality generalization under real-world conditions, where GED4 hepatobiliary-phase annotations are limited, non-contrast sequences (T1WI, T2WI, DWI) are unlabeled, and spatial misalignment and missing phases are common. Our method integrates a foundation-scale 3D segmentation backbone adapted via fine-tuning, co-training with cross pseudo supervision to leverage unlabeled volumes, and a standardized preprocessing pipeline. Without requiring spatial registration, the model learns to generalize across MRI phases and vendors, demonstrating robust segmentation performance in both labeled and unlabeled domains. Our results exhibit the effectiveness of our proposed label-efficient baseline for liver segmentation in multi-phase, multi-vendor MRI and highlight the potential of combining foundation model adaptation with co-training for real-world clinical imaging tasks.

翻译：多期相MRI中的精确肝脏分割对于肝纤维化评估至关重要，但标注数据往往稀缺且在不同成像模态和厂商系统间分布不均。我们提出了一种标签高效的分割方法，旨在促进真实世界条件下的跨模态泛化，其中GED4肝胆期标注有限，非对比序列（T1WI、T2WI、DWI）未标注，且空间错位与缺失期相普遍存在。我们的方法整合了通过微调适配的基础规模3D分割主干网络、利用未标注体数据进行交叉伪监督的协同训练，以及标准化的预处理流程。无需空间配准，该模型能够学习跨MRI期相和厂商的泛化能力，在标注和未标注域均展现出鲁棒的分割性能。我们的结果证明了所提出的标签高效基线方法在多期相、多厂商MRI肝脏分割中的有效性，并突显了结合基础模型适配与协同训练在真实世界临床影像任务中的潜力。