Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion.

ABSTRACT: Infarct size (IS) is the most robust end point for evaluating the success of preclinical studies on cardioprotection. The gold standard for IS quantification in ischemia/reperfusion (I/R) experiments is triphenyl tetrazolium chloride (TTC) staining, typically done manually. This study aimed to determine if automation through deep learning segmentation is a time-saving and valid alternative to standard IS quantification. High-resolution images from TTC-stained, macroscopic heart slices were retrospectively collected from pig experiments (n = 390) with I/R without/with cardioprotection to cover a wide IS range. Existing IS data from pig experiments, quantified using a standard method of manual and subsequent digital labeling of film-scan annotations, were used as reference. To automate the e

SUBMITTER: Braczko F 

PROVIDER: S-EPMC11628591 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature