用混合向量集建模心脏四腔结构，能补全缺失部分并生成动态影像。
VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSets
- 引入分部可学习查询与交错注意力，捕捉心腔间复杂关系。
- 在部分、稀疏或缺失数据下仍能重建完整心脏结构。
- 支持三维动态网格序列生成，适合医学影像分析场景。
精准的心脏解剖建模需处理结构间的复杂关联。本文提出VecHeart，一个统一框架，用于整体重建与生成四腔心脏结构。为克服现有前馈隐式方法仅限单对象建模且忽略部件间相关性的局限，我们引入混合部件变换器（Hybrid Part Transformer），利用部件特异性可学习查询与交错注意力机制，捕获复杂的心腔依赖关系。此外，提出解剖补全掩码与模态对齐策略，使模型能在部分、稀疏或噪声观测下，甚至在某些解剖部分完全缺失时，仍能推断出完整的四腔结构。该方法还可无缝扩展至3D+t动态网格序列生成，展现卓越泛化能力。实验表明，本方法在多种挑战性场景下均达到当前最优性能，保持高保真重建。代码已开源：https://github.com/Scalsol/VecHeart。
原文摘要 · Abstract (English)
Accurate cardiac anatomy modeling requires the model to be able to handle intricate interrelations among structures. In this paper, we propose VecHeart, a unified framework for holistic reconstruction and generation of four-chamber cardiac structures. To overcome the limitations of current feed-forward implicit methods, specifically their restriction to single-object modeling and their neglect of inter-part correlations, we introduce Hybrid Part Transformer, which leverages part-specific learnable queries and interleaved attention to capture complex inter-chamber dependencies. Furthermore, we propose Anatomical Completion Masking and Modality Alignment strategies, enabling the model to infer complete four-chamber structures from partial, sparse, or noisy observations, even when certain anatomical parts are entirely missing. VecHeart also seamlessly extends to 3D+t dynamic mesh sequence generation, demonstrating exceptional versatility. Experiments show that our method achieves state-of-the-art performance, maintaining high-fidelity reconstruction across diverse challenging scenarios. Code is available at https://github.com/Scalsol/VecHeart.
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