Organizations: Academy of Artificial Intelligence and Advanced Technology, Xi’an Jiaotong-Liverpool University, China · Department of Health Technology and Informatics, The Hong Kong Polytechnic University, China · Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, UK
Three-dimensional (3D) left ventricular (LV) reconstruction from sparse cardiac magnetic resonance (CMR) imaging remains challenging due to inter-slice misalignment and insufficient local spatial information between slices. Global aggregation of contour features may obscure local contour-to-surface relationships. We propose Local2Mesh, a spatially localized contour-to-mesh framework that deforms a template mesh to reconstruct 3D LV geometry from sparse 2D contours without 3D mesh annotations. The framework introduces geometry-aware alignment to correct inter-slice misalignment and a plane-aware Local Router that routes contour features to template vertices using vertex-to-plane distances. Local and global contour features then jointly guide graph-based template deformation for 3D LV reconstruction. Experiments on two public datasets, M&Ms-2 and ACDC, demonstrate superior geometric reconstruction and functional estimation over existing methods. Zero-shot transfer from M&Ms-2 to ACDC demonstrates strong cross-dataset generalization. Reconstructed meshes also improve disease classification over sparse contours, supporting their utility for downstream cardiac analysis. These results demonstrate that combining geometry-aware alignment with local contour-to-vertex modeling improves LV reconstruction from sparse 2D contours and supports downstream cardiac analysis. The code is available at https://github.com/hwu918945-alt/loca2mesh.
Figures & tables
Figure 1: Overview of Local2Mesh. Aligned 2D contours are encoded into global and surface-specific features, which are routed to template vertices and integrated to condition graph-based deformation for patient-specific LV reconstruction.
Figure 2: Qualitative comparison of LV reconstruction.
Input / Classifier
Params
ACC ↑
F1 ↑
AUC ↑
Dense mesh + PointNet
4.00M
80.00
78.14
92.78
Sparse contours + PointNet
4.00M
46.67
47.76
90.00
MRI + masks + ResNet-18
33.43M
73.33
69.29
84.44
Table 4: Disease classification results on ACDC (%).
Department of Biomedical Engineering, National University of Singapore, Singapore · School of Automation, Southeast University, Nanjing, China · Department of Medicine, National University of Singapore, Singapore +1