ScarNet: a novel foundation model for automated myocardial scar quantification from late gadolinium-enhancement images.

ABSTRACT:

Background

Late gadolinium enhancement (LGE) imaging remains the gold standard for assessing myocardial fibrosis and scarring, with left ventricular (LV) LGE presence and extent serving as a predictor of major adverse cardiac events (MACE). Despite its clinical significance, LGE-based LV scar quantification is not used routinely due to the labor-intensive manual segmentation and substantial inter-observer variability.

Methods

We developed ScarNet that synergistically combines a transformer-based encoder in medical segment anything model (MedSAM), which we fine-tuned with our dataset, and a convolution-based decoder in UNet with tailored attention blocks to automatically segment myocardial scar boundaries while maintaining anatomical context. This network was trained and fine

SUBMITTER: Tavakoli N 

PROVIDER: S-EPMC12681538 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature