VecHeart：通过混合VecSets实现整体四腔心脏解剖建模
VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSets
AI总结：
本文提出VecHeart框架，通过混合部分Transformer和解剖完成遮蔽策略，实现四腔心脏结构的完整重建与生成，解决了传统方法在多部分关联建模中的不足。
摘要：
准确的心脏解剖建模需要模型能够处理结构间的复杂相互关系。本文提出VecHeart，一个统一的框架，用于整体重建和生成四腔心脏结构。为克服当前前馈隐式方法的局限性，特别是其对单对象建模的限制和对部分间相关性的忽视，我们引入了混合部分Transformer，利用部分特定的可学习查询和交错注意力来捕捉复杂的室间依赖关系。此外，我们提出了解剖完成遮蔽和模态对齐策略，使模型能够从部分、稀疏或噪声观测中推断出完整的四腔结构，即使某些解剖部分完全缺失。VecHeart还无缝扩展到3D+t动态网格序列生成，展示了卓越的通用性。实验表明，我们的方法在多种具有挑战性的场景中实现了最先进的性能。代码将被发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
