用基础模型融合多视角多序列心脏影像，实现精准分割与射血分数直接预测。
Foundation Models Adaptation for Multi-View Multi-modal Cardiac MRI Segmentation and Direct Ejection Fraction Estimation
- 融合多个基础模型，通过微调和注意力机制处理多视图多序列数据。
- 短轴、两腔、四腔电影图像分割Dice达0.862~0.902，LVEF预测误差仅4.96%。
- 适合医疗影像分析者、心血管疾病研究者快速应用基础模型于复杂心脏影像任务。
基础模型在心脏磁共振（CMR）中展现强迁移能力，但其在异构多视角、多序列CMR分析中的有效性尚不明确。本文探索微调与组合不同CMR基础模型在通用多序列、多中心、多视图CMR分割挑战中的表现。CineMA模型在短轴与长轴视图上对电影成像和延迟增强（LGE）进行微调分割。针对左心室射血分数（LVEF）的直接估计，采用两个冻结的近期CMR基础模型提取嵌入向量，并通过基于注意力的多实例学习进行融合回归。在挑战验证集上，电影分割的Dice得分分别为短轴0.862、两腔0.883、四腔0.902；LGE分割得分在0.621至0.846之间。直接LVEF回归模型达到4.96个百分点的平均绝对误差和0.91的皮尔逊相关系数。结果表明，基础模型可有效适配并组合用于多视角CMR分析，而准确的LGE瘢痕分割仍是挑战。
原文摘要 · Abstract (English)
Foundation models have shown strong transferability in cardiac MRI (CMR), but their effectiveness for heterogeneous multi-view and multi-sequence CMR analysis remains unclear. In this work, we explore the effectiveness of fine-tuning and combining different CMR foundation models for the Universal Multi-Sequence, Multi-Center and Multi-View CMR Segmentation (CMR-Multi) Challenge. CineMA was fine-tuned for cine and late gadolinium enhancement (LGE) segmentation across short-axis and long-axis views. For direct left-ventricular ejection fraction (LVEF) estimation, we used two recent frozen CMR foundation models to extract embedding vectors that were then combined using attention-based multiple-instance learning for LVEF regression. In the challenge validation set, cine segmentation achieved Dice scores of 0.862, 0.883, and 0.902 for short-axis, two-chamber and four-chamber cine MRI, respectively. LGE segmentation achieved Dice scores between 0.621 and 0.846 across views. The direct LVEF regression model achieved an MAE of 4.96 percentage points and a Pearson correlation of 0.91. These results indicate that foundation models can be effectively adapted and combined for multi-view CMR analysis, while accurate LGE scar segmentation remains a challenging task.
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