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：多期相磁共振成像（MRI）中的精确肝脏分割对于肝纤维化评估至关重要，然而标注数据往往稀缺且在成像模态与厂商系统间分布不均。本文提出一种标签高效的分割方法，旨在促进真实临床条件下的跨模态泛化：其中GED4肝胆期标注有限，非增强序列（T1WI、T2WI、DWI）无标注，且空间错位与期相缺失现象普遍。我们的方法整合了经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策略，以及标准化预处理流程。该模型无需空间配准即可学习跨MRI期相与厂商的泛化能力，在标注与未标注域均表现出稳健的分割性能。实验结果验证了所提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，并凸显了基础模型适配与协同训练相结合在真实临床影像任务中的潜力。