Mamba-Based VoxelMorph Framework for Cardiovascular Disease Imaging and Risk Assessment
Cardiovascular diseases (CVDs), particularly coronary artery disease (CAD), remain the leading cause of global mortality, necessitating advanced diagnostic solutions. Accurate deformable image registration plays a crucial role in enhancing segmentation precision and classification performance in car...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10979302/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Cardiovascular diseases (CVDs), particularly coronary artery disease (CAD), remain the leading cause of global mortality, necessitating advanced diagnostic solutions. Accurate deformable image registration plays a crucial role in enhancing segmentation precision and classification performance in cardiovascular imaging. However, existing registration methods, including VoxelMorph, face limitations in computational efficiency and memory usage, restricting their real-time applicability for high-resolution cardiac imaging. This study proposes the Mamba-Optimized VoxelMorph framework, which leverages GPU-based parallelization and memory optimization to address these challenges. The framework achieves superior registration accuracy, yielding a Dice Similarity Coefficient (DSC) of 0.95 and Normalized Cross-Correlation (NCC) of 0.90, while reducing computational time by 40% and memory usage to 800 MB. These advancements ensure efficient alignment of complex cardiac structures, thereby improving segmentation accuracy and classification reliability. By addressing these critical limitations, the Mamba-Optimized VoxelMorph framework significantly enhances cardiovascular imaging, enabling precise, scalable, and real-time deformable image registration for improved CAD diagnosis and treatment planning. |
|---|---|
| ISSN: | 2169-3536 |