HeartFormer: Semantic-Aware Dual-Structure Transformers for 3D Four-Chamber Cardiac Point Cloud Reconstruction
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917113632915456 |
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| author | Ma, Zhengda Banerjee, Abhirup |
| author_facet | Ma, Zhengda Banerjee, Abhirup |
| contents | We present the first geometric deep learning framework based on point cloud representation for 3D four-chamber cardiac reconstruction from cine MRI data. This work addresses a long-standing limitation in conventional cine MRI, which typically provides only 2D slice images of the heart, thereby restricting a comprehensive understanding of cardiac morphology and physiological mechanisms in both healthy and pathological conditions. To overcome this, we propose \textbf{HeartFormer}, a novel point cloud completion network that extends traditional single-class point cloud completion to the multi-class. HeartFormer consists of two key components: a Semantic-Aware Dual-Structure Transformer Network (SA-DSTNet) and a Semantic-Aware Geometry Feature Refinement Transformer Network (SA-GFRTNet). SA-DSTNet generates an initial coarse point cloud with both global geometry features and substructure geometry features. Guided by these semantic-geometry representations, SA-GFRTNet progressively refines the coarse output, effectively leveraging both global and substructure geometric priors to produce high-fidelity and geometrically consistent reconstructions. We further construct \textbf{HeartCompv1}, the first publicly available large-scale dataset with 17,000 high-resolution 3D multi-class cardiac meshes and point-clouds, to establish a general benchmark for this emerging research direction. Extensive cross-domain experiments on HeartCompv1 and UK Biobank demonstrate that HeartFormer achieves robust, accurate, and generalizable performance, consistently surpassing state-of-the-art (SOTA) methods. Code and dataset will be released upon acceptance at: https://github.com/10Darren/HeartFormer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00264 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HeartFormer: Semantic-Aware Dual-Structure Transformers for 3D Four-Chamber Cardiac Point Cloud Reconstruction Ma, Zhengda Banerjee, Abhirup Computer Vision and Pattern Recognition We present the first geometric deep learning framework based on point cloud representation for 3D four-chamber cardiac reconstruction from cine MRI data. This work addresses a long-standing limitation in conventional cine MRI, which typically provides only 2D slice images of the heart, thereby restricting a comprehensive understanding of cardiac morphology and physiological mechanisms in both healthy and pathological conditions. To overcome this, we propose \textbf{HeartFormer}, a novel point cloud completion network that extends traditional single-class point cloud completion to the multi-class. HeartFormer consists of two key components: a Semantic-Aware Dual-Structure Transformer Network (SA-DSTNet) and a Semantic-Aware Geometry Feature Refinement Transformer Network (SA-GFRTNet). SA-DSTNet generates an initial coarse point cloud with both global geometry features and substructure geometry features. Guided by these semantic-geometry representations, SA-GFRTNet progressively refines the coarse output, effectively leveraging both global and substructure geometric priors to produce high-fidelity and geometrically consistent reconstructions. We further construct \textbf{HeartCompv1}, the first publicly available large-scale dataset with 17,000 high-resolution 3D multi-class cardiac meshes and point-clouds, to establish a general benchmark for this emerging research direction. Extensive cross-domain experiments on HeartCompv1 and UK Biobank demonstrate that HeartFormer achieves robust, accurate, and generalizable performance, consistently surpassing state-of-the-art (SOTA) methods. Code and dataset will be released upon acceptance at: https://github.com/10Darren/HeartFormer. |
| title | HeartFormer: Semantic-Aware Dual-Structure Transformers for 3D Four-Chamber Cardiac Point Cloud Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.00264 |