1. Uncertainty Quantification of the Effects of Segmentation Variability in ECGI
- Author
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Rob S. MacLeod, Wilson W. Good, Machteld J Boonstra, Jess D. Tate, Dana H. Brooks, Akil Narayan, Nejib Zemzemi, Peter M. van Dam, University of Utah, Modélisation et calculs pour l'électrophysiologie cardiaque (CARMEN), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IHU-LIRYC, Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-CHU Bordeaux [Bordeaux], Utrecht Brain Center [UMC], University Medical Center [Utrecht], Northeastern University [Boston], Supported by the National Institutes of Health, P41GM103545, R24GM136986, U24EB029012, U24EB029011, R01AR076120, and R01HL135568, Daniel B. Ennis, Luigi E. Perotti, and Vicky Y. Wang
- Subjects
Electrocardiographic Imaging ,Shape Analysis ,Polynomial chaos ,business.industry ,Computer science ,Pipeline (computing) ,High variability ,Pattern recognition ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Article ,Standard deviation ,Electrocardiographic imaging ,Uncertainty Quantification ,Segmentation ,Artificial intelligence ,Uncertainty quantification ,business ,Shape analysis (digital geometry) - Abstract
International audience; 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.
- Published
- 2021