Why the study?
Manual myocardial scar segmentation is time-consuming and operator-dependent, and consensus is lacking on semi-automatic segmentation methods for black-blood LGE imaging.
Can conventional semi-automatic scar segmentation algorithms be accurately applied to black-blood late gadolinium enhancement images in patients with ischemic heart disease?
Comparison
Semi-automatic scar segmentation methods (n-SD, FWHM, and Otsu) on black-blood SPOT vs manual segmentation
Design
Observational imaging study
Key result
Conventional semi-automatic scar segmentation algorithms can be applied to black-blood imaging, with the 6-SD method achieving the highest Dice Similarity Coefficient (81.3%).
Authors
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Supports feasibility of 6-SD segmentation on black-blood LGE; leaves open prospective validation before clinical use.
Observational (n=20)
Can conventional semi-automatic scar segmentation algorithms be accurately applied to black-blood late gadolinium enhancement images in patients with ischemic heart disease?
Absolute Event Rate: 81.3% vs 87.9%
Conventional semi-automatic scar segmentation algorithms developed for bright-blood imaging can be successfully applied to black-blood LGE imaging, with the 6-SD method showing the highest accuracy.
Génisson et al. (2026) conducted an observational in Ischemic heart disease (n=20). Semi-automatic scar segmentation algorithms (n-SD, FWHM, Otsu) vs. Manual segmentation was evaluated on Dice Similarity Coefficient (DSC). Conventional semi-automatic scar segmentation algorithms can be applied to black-blood imaging, with the 6-SD method achieving the highest Dice Similarity Coefficient (81.3%).