Cardiac MRI Segmentation

Cardiac MRI segmentation, the automated identification of heart structures in MRI scans, is crucial for accurate diagnosis and treatment of cardiovascular diseases. Research focuses on improving segmentation accuracy and robustness using deep learning models like nnU-Net and vision transformers, while also addressing challenges such as bias stemming from imbalanced datasets and the need for efficient annotation strategies. Current efforts explore techniques like contrastive learning and Bayesian frameworks to enhance model generalizability and calibration, ultimately aiming to improve the reliability and clinical utility of automated cardiac MRI analysis. This work has significant implications for streamlining clinical workflows and improving patient care.