Uncertainty Quantification of the Effects of Segmentation Variability in ECGI

Abstract

Despite advances in many of the techniques used in Electrocardiographic Imaging (ECGI), uncertainty remains insufficiently quantified for many aspects of the pipeline. The effect of geometric uncertainty, particularly due to segmentation variability, may be the least explored to date. We use statistical shape modeling and uncertainty quantification (UQ) to compute the effect of segmentation variability on ECGI solutions. The shape model was made with Shapeworks from nine segmentations of the same patient and incorporated into an ECGI pipeline. We computed uncertainty of the pericardial potentials and local activation times (LATs) using polynomial chaos expansion (PCE) implemented in UncertainSCI. Uncertainty in pericardial potentials from segmentation variation mirrored areas of high variability in the shape model, near the base of the heart and the right ventricular outflow tract, and that ECGI was less sensitive to uncertainty in the posterior region of the heart. Subsequently LAT calculations could vary dramatically due to segmentation variability, with a standard deviation as high as 126ms, yet mainly in regions with low conduction velocity. Our shape modeling and UQ pipeline presented possible uncertainty in ECGI due to segmentation variability and can be used by researchers to reduce said uncertainty or mitigate its effects. The demonstrated use of statistical shape modeling and UQ can also be extended to other types of modeling pipelines.

Supported by the National Institutes of Health, P41GM103545, R24GM136986, U24EB029012, U24EB029011, R01AR076120, and R01HL135568. Data used in this study was made available by Drs. John Sapp and Milan Horáček and their research collaboration with Dalhousie University. Thanks to Sophie Giffard-Roisin, Eric Perez-Alday, Laura Bear, Beáta Ondrušová, Svehlikova, Machteld Boonstra, Martim Kastelein, and Maryam Tolou for providing segmentations for this study.