Name
Abstract | ||
|---|---|---|
Patient-specific cardiac simulation rely on accurate geometric models extracted from medical images. Segmentation of cardiac images is a key, yet possibly error-prone part of patient-specific simulations, e.g., heart propagation models, ECG forward simulation, and ECG Imaging. In this study, we performed shape analysis on multiple segmentations of the same patient to quantify variability. We found that segmentation shape varied most in the basal region of the ventricles and the right ventricular outflow tract in all three structures, which could have significant impact on pipelines that depend on geometric models. The statistical shape-model generated using ShapeWorks provides a pathway to subsequently quantify the impact of the segmentation variability on modeling pipelines with uncertainty quantification. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.22489/CinC.2020.466 | 2020 Computing in Cardiology |
Keywords | DocType | ISSN |
segmentation variability,patient-specific cardiac simulation,geometric models,medical images,cardiac image segmentation,ECG Imaging,right ventricular outflow tract,statistical shape model,basal region,ShapeWorks,ECG forward simulation,heart propagation model | Conference | 2325-8861 |
ISBN | Citations | PageRank |
978-1-7281-1105-6 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jess D. Tate | 1 | 0 | 0.34 |
| Nejib Zemzemi | 2 | 0 | 0.34 |
| Wilson W. Good | 3 | 0 | 0.34 |
| Peter M. van Dam | 4 | 0 | 0.34 |
| Dana H Brooks | 5 | 215 | 61.52 |
| Rob S. MacLeod | 6 | 0 | 0.34 |