Automated left ventricle segmentation in late gadolinium-enhanced MRI for objective myocardial scar assessment

To develop and validate an objective and reproducible left ventricle (LV) segmentation method for late gadolinium enhanced (LGE) magnetic resonance imaging (MRI), which can facilitate accurate myocardial scar assessment.

A cohort of 25 ischemic patients and 25 nonischemic patients were included. A four-step algorithm was proposed: first, the Cine-MRI and LGE-MRI volume were globally registered; second, the registered Cine-MRI contours were fitted to each LGE-MRI slice via the constructed contour image; third, the fitting was optimized in full LGE-MRI stack; finally, the contours were refined by taking into account patient-specific scar patterns. The automated LV segmentation results were compared with that of manual segmentation from two experienced observers.

The accuracy of automated segmentation, expressed as the average contour distances to manual segmentation, was 0.82 ± 0.19 pixels, in the same order as interobserver difference between manual results (0.90 ± 0.26 pixels), but with lower variability (0.60 ± 0.37 pixels, P < 0.05). The myocardial scar identification based on automated LV segmentation further demonstrated higher consistency than that of manual segmentation (Pearson correlation 0.97 vs. 0.84).

An automated LV segmentation method for LGE-MRI was developed, providing high segmentation accuracy and lower interobserver variability compared to fully manual image analysis. The method facilitates objective assessment of myocardial scar.