VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSets
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910153599614976 |
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| author | Chen, Yihong Fua, Pascal |
| author_facet | Chen, Yihong Fua, Pascal |
| contents | 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 will be released. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_19403 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSets Chen, Yihong Fua, Pascal Computer Vision and Pattern Recognition 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 will be released. |
| title | VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSets |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.19403 |