AI-Driven Scribble-Based Foundation Model for Left Ventricular Scar Quantification on cardiac MRI

Motivation: Late Gadolinium Enhancement imaging is the gold standard for assessing myocardial fibrosis, with LV scar volume as a key predictor of major adverse cardiac events. However, manual segmentation is labor-intensive and variable, limiting practical use. Goal(s): Develop an automated deep learning method for accurate LV scar quantification, tackling complex scar appearances and incorporating a scribble GUI for refining scar segmentation. Approach: A foundation model combining MedSAM's representation capabilities with U-Net's localization, enhanced by scribble-based annotations. Results: The deep learning model achieved an average scar Dice score of 0.917 for connected scars and 0.719 for disconnected scars, surpassing existing methods. Impact: Our foundation model offers a significant advancement in automated LV scar assessment, improving reliability, reducing manual workload, and enhancing consistency in clinical cardiac imaging, which can lead to better patient outcomes through timely and accurate diagnosis.