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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.19862 |
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| _version_ | 1866908506026672128 |
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| author | Chen, Long Patel, Ashiv Qiao, Mengyun Salmasi, Mohammad Yousuf Hammouche, Salah A. Stavrinides, Vasilis Nagi, Jasleen Kalaie, Soodeh Xu, Xiao Yun Bai, Wenjia O'Regan, Declan P. |
| author_facet | Chen, Long Patel, Ashiv Qiao, Mengyun Salmasi, Mohammad Yousuf Hammouche, Salah A. Stavrinides, Vasilis Nagi, Jasleen Kalaie, Soodeh Xu, Xiao Yun Bai, Wenjia O'Regan, Declan P. |
| contents | Personalized, accurate prediction of aortic aneurysm progression is essential for timely intervention but remains challenging due to the need to model both subtle local deformations and global anatomical changes within complex 3D geometries. We propose MCMeshGAN, the first multimodal conditional mesh-to-mesh generative adversarial network for 3D aneurysm growth prediction. MCMeshGAN introduces a dual-branch architecture combining a novel local KNN-based convolutional network (KCN) to preserve fine-grained geometric details and a global graph convolutional network (GCN) to capture long-range structural context, overcoming the over-smoothing limitations of deep GCNs. A dedicated condition branch encodes clinical attributes (age, sex) and the target time interval to generate anatomically plausible, temporally controlled predictions, enabling retrospective and prospective modeling. We curated TAAMesh, a new longitudinal thoracic aortic aneurysm mesh dataset consisting of 590 multimodal records (CT scans, 3D meshes, and clinical data) from 208 patients. Extensive experiments demonstrate that MCMeshGAN consistently outperforms state-of-the-art baselines in both geometric accuracy and clinically important diameter estimation. This framework offers a robust step toward clinically deployable, personalized 3D disease trajectory modeling. The source code for MCMeshGAN and the baseline methods is publicly available at https://github.com/ImperialCollegeLondon/MCMeshGAN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19862 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Conditional MeshGAN for Personalized Aneurysm Growth Prediction Chen, Long Patel, Ashiv Qiao, Mengyun Salmasi, Mohammad Yousuf Hammouche, Salah A. Stavrinides, Vasilis Nagi, Jasleen Kalaie, Soodeh Xu, Xiao Yun Bai, Wenjia O'Regan, Declan P. Computer Vision and Pattern Recognition Machine Learning Personalized, accurate prediction of aortic aneurysm progression is essential for timely intervention but remains challenging due to the need to model both subtle local deformations and global anatomical changes within complex 3D geometries. We propose MCMeshGAN, the first multimodal conditional mesh-to-mesh generative adversarial network for 3D aneurysm growth prediction. MCMeshGAN introduces a dual-branch architecture combining a novel local KNN-based convolutional network (KCN) to preserve fine-grained geometric details and a global graph convolutional network (GCN) to capture long-range structural context, overcoming the over-smoothing limitations of deep GCNs. A dedicated condition branch encodes clinical attributes (age, sex) and the target time interval to generate anatomically plausible, temporally controlled predictions, enabling retrospective and prospective modeling. We curated TAAMesh, a new longitudinal thoracic aortic aneurysm mesh dataset consisting of 590 multimodal records (CT scans, 3D meshes, and clinical data) from 208 patients. Extensive experiments demonstrate that MCMeshGAN consistently outperforms state-of-the-art baselines in both geometric accuracy and clinically important diameter estimation. This framework offers a robust step toward clinically deployable, personalized 3D disease trajectory modeling. The source code for MCMeshGAN and the baseline methods is publicly available at https://github.com/ImperialCollegeLondon/MCMeshGAN. |
| title | Multimodal Conditional MeshGAN for Personalized Aneurysm Growth Prediction |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2508.19862 |