Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization

Narimane Gassa1, Nejib Zemzemi2, Machteld Boonstra3, Beata Ondrusova4, Jana Svehlikova5, Dana Brooks6, Ali Rababah7, Rob MacLeod8, Jess Tate8, Peter Van Dam3, Akil Narayan8
1University of Bordeaux, 2Inria Bordeaux Sud-Ouest, 3University Medical Center Utrecht, 4Institute of Measurement Science, 5Institute of Measurement Science, SAS, 6Northeastern University, 7Jordanian Royal Medical Services, 8University of Utah


Electrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) and the patient specific anatomical data.    One of the first steps of ECGI is the segmentation of heart and torso. However, the inter-operator variation in cardiac segmentations may influence the ECGI solution. This effect reamains however unquantified. In this work, we study the effect of segmentation uncertainty on the ECGI estimation of the cardiac activity with 262 shape models generated from fifteen different segmentations. Our results confirm that this variation in cardiac segmentation induces an additional error in the ECGI solution. Moreover, these results also indicate that septal stimulation is more sensitive to segmetation variability than LV, RV and apical stimulation.