Overview
AI segmentation automates delineation of cardiac chambers and vessels. It reduces manual contouring time and improves reproducibility. Quantitative metrics support clinical decision making.
Techniques
Deep learning models such as convolutional networks perform segmentation tasks. Training requires high quality labeled datasets and augmentation strategies. Post processing refines contours for clinical use.
Clinical Applications
Automated segmentation supports volumetric analysis and ejection fraction calculation. It aids in planning interventions and monitoring therapy. Integration with reporting systems streamlines workflows.
Validation
Validation includes comparison with expert manual contours and inter observer studies. Robustness across scanners and pathologies is essential. Regulatory clearance depends on demonstrated clinical benefit.