Automated cardiovascular MR myocardial scar quantification with unsupervised domain adaptation.

ABSTRACT: Quantification of myocardial scar from late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) images can be facilitated by automated artificial intelligence (AI)-based analysis. However, AI models are susceptible to domain shifts in which the model performance is degraded when applied to data with different characteristics than the original training data. In this study, CycleGAN models were trained to translate local hospital data to the appearance of a public LGE CMR dataset. After domain adaptation, an AI scar quantification pipeline including myocardium segmentation, scar segmentation, and computation of scar burden, previously developed on the public dataset, was evaluated on an external test set including 44 patients clinically assessed for ischemic scar. The mean ±

SUBMITTER: Crawley R 

PROVIDER: S-EPMC11324636 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature