用少量标注数据实现多期多厂商MRI肝脏分割，无需配准。
Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI
- 基于微调的3D基础模型+伪标签协同训练，利用无标注数据增强泛化能力。
- 在仅有4例肝胆期标注的情况下，分割准确率达87.3%（Dice）。
- 适合标注稀缺、设备多样、图像错位的真实临床场景使用。
多期MRI中精确分割肝脏对肝纤维化评估至关重要，但标注数据常稀缺且在不同成像模态和设备间分布不均。本文提出一种标签高效分割方法，在真实条件下实现跨模态泛化：仅有限的GED4肝胆期标注，非增强序列（T1WI、T2WI、DWI）为无标注，且存在空间错位与缺相问题。方法结合经微调的基础规模3D分割骨干网络、跨伪监督协同训练以利用无标注体积，以及标准化预处理流程。无需空间配准，模型可在不同期相与设备间有效泛化，表现出在标注与未标注域中均稳健的分割性能。结果验证了该标签高效基线在多期、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结合在真实临床影像任务中的潜力。
原文摘要 · Abstract (English)
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.
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