Comparison of semi-automatic myocardial scar segmentation methods on black-blood late gadolinium enhancement images

PURPOSE: Myocardial scar quantification is key for patient prognosis. Black-blood late gadolinium enhancement (LGE) imaging improves scar visualization by nulling both blood and myocardium, solving the blood-scar interface contrast issue of bright-blood LGE. With no consensus on semi-automatic methods, scar segmentation is often manual, and therefore time-consuming and operator-dependent. We evaluate semi-automatic scar segmentation algorithms on co-registered bright- and black-blood LGE images. METHOD: Twenty patients with ischemic heart disease were included. Breath-held short-axis 2D whole-heart joint bright- and black-blood SPOT and reference PSIR images were collected on a 1.5 T system in mid-diastole. Left ventricle wall and scar were manually segmented by two experts on both images. The n-SD (n from 2 to 15), FWHM and Otsu thresholding scar segmentation methods were applied to black-blood SPOT images. Dice Similarity Coefficient (DSC), scar extent, volume and transmurality were measured for all segmentations. RESULTS: Manual scar segmentation DSCs are higher on SPOT than PSIR images at both intra- (DSC: 87.9%[85.1%-88.8%]) and interobserver (87.3% [79.4%-89.4%]) levels. SPOT and PSIR yielded similar scar extent and volumes with moderate to good agreement (0.59 < ICC < 0.77). The 6-SD achieved the highest DSC (81.3%[73.7%-76.8%]), the most accurate scar extent (bias(SD): -0.8%(9.6%)), scar volume (bias(SD): 0.5 mL(7.1 mL)) and scar transmurality (bias(SD): -0.29%(10.70%)) for 7-, 8- and 3-SD, respectively. CONCLUSION: Conventional semi-automatic scar segmentation algorithms developed for bright-blood imaging can be applied to black-blood imaging. Validation in larger populations is needed to determine and refine the most accurate approach.