Bibliographic Details
| Title: |
Topology-Preserving Loss for Accurate and Anatomically Consistent Cardiac Mesh Reconstruction |
| Authors: |
Zhang, Chenyu, Luo, Yihao, Wu, Yinzhe, Yap, Choon Hwai, Yang, Guang |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence |
| More Details: |
Accurate cardiac mesh reconstruction from volumetric data is essential for personalized cardiac modeling and clinical analysis. However, existing deformation-based approaches are prone to topological inconsistencies, particularly membrane penetration, which undermines the anatomical plausibility of the reconstructed mesh. To address this issue, we introduce Topology-Preserving Mesh Loss (TPM Loss), a novel loss function that explicitly enforces topological constraints during mesh deformation. By identifying topology-violating points, TPM Loss ensures spatially consistent reconstructions. Extensive experiments on CT and MRI datasets show that TPM Loss reduces topology violations by up to 93.1% while maintaining high segmentation accuracy (DSC: 89.1%-92.9%) and improving mesh fidelity (Chamfer Distance reduction up to 0.26 mm). These results demonstrate that TPM Loss effectively prevents membrane penetration and significantly improves cardiac mesh quality, enabling more accurate and anatomically consistent cardiac reconstructions. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2503.07874 |
| Accession Number: |
edsarx.2503.07874 |
| Database: |
arXiv |
