Ventricle Segmentation

Ventricle segmentation, the automated identification of heart chambers in medical images, aims to improve the accuracy and efficiency of cardiac diagnosis and treatment planning. Current research focuses on addressing challenges like inconsistent annotations, especially in the complex right ventricle base, and mitigating artifacts from image acquisition. Popular approaches leverage deep learning architectures, including U-Net variants and transformer networks, often incorporating techniques like uncertainty estimation, domain adaptation, and contrastive learning to enhance robustness and performance. These advancements hold significant promise for improving the reproducibility and clinical utility of cardiac image analysis.