ArticleFunctional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference)2021
Uncertainty Quantification of the Effects of Segmentation Variability in ECGI.
Article in Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- Uncertainty Quantification in Medical Image Segmentation: A Comprehensive Survey.Journal of imaging · 2026Review
- Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing.Medical image analysis · 2025Article
- Quantifying and Visualizing Uncertainty for Source Localization in Electrocardiographic Imaging.Computer methods in biomechanics and biomedical engineering. Imaging & visualization · 2023Article
- UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering.Computers in biology and medicine · 2023Article
- Novel non-invasive ECG imaging method based on the 12-lead ECG for reconstruction of ventricular activation: A proof-of-concept study.Frontiers in cardiovascular medicine · 2023Article
- Basis and applicability of noninvasive inverse electrocardiography: a comparison between cardiac source models.Frontiers in physiology · 2023Review
- Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization.Computing in cardiology · 2022Article
- The Effect of Segmentation Variability in Forward ECG Simulation.Computing in cardiology · 2022Article
- Reconstruction of cardiac position using body surface potentials.Computers in biology and medicine · 2022Article
- Body Surface Potential Mapping: Contemporary Applications and Future Perspectives.Hearts (Basel, Switzerland) · 2021Article
- A Cardiac Shape Model for Segmentation Uncertainty Quantification.Computing in cardiology · 2021Article
- Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods.Frontiers in physiology · 2021Article
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
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.
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