This workgroup was established during the meeting at computing in Cardiology 2015. The focus of this workgroup is to study the effect of errors and uncertainty associated with model building techniques in computing forward and inverse ECG solutions. The research consists of comparing the three phases to calculate the forward or inverse solution: segmentation of medical imaging, mesh computation and the construction of the transfer matrix. Research is currently ongoing, for more information about the current status of the work or questions please contact the group organizer.

Group organizer: Machteld Boonstra (cei-modelbuilding@googlegroups.com)

Members: Machteld Boonstra, Jess Tate, Ali Rababah, Wilson Good, Laura Bear, Jana Svehlikova, Peter van Dam, Machteld Boonstra, Rob MacLeod, Sanne Groeneveld, Joselin Duchateau, Jose Luis Rojo, Beata Ondrusova, Nejib Zemzemi, Dana Brooks.

Publications:

1. Narimane Gassa, Machteld Boonstra, Beata Ondrusoval, Jana Svehlikova, Dana Brooks, Akil Narayan, Ali Salman Rababah, Peter van Dam, Rob MacLeod, Jess Tate, Nejib Zemzemi, Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization, in: 2022 Computing in Cardiology (CinC)

2. Jess D Tate, Nejib Zemzemi, Shireen Elhabian, Beáta Ondrušová, Machteld Boonstra, Peter van Dam, Akil Narayan, Dana H Brooks, Rob S MacLeod, Segmentation Uncertainty Quantification in Cardiac Propagation Models, in: 2022 Computing in Cardiology (CinC)

3. Beata Ondrusova, Machteld Boonstra, Jana Svehlikova, Dana Brooks, Peter van Dam, Ali Salman Rababah, Akil Narayan, Rob MacLeod, Nejib Zemzemi, Jess Tate, The Effect of Segmentation Variability in Forward ECG Simulation, in: 2022 Computing in Cardiology (CinC)

4. Jess D Tate, Shireen Elhabian, Nejib Zemzemi, Wilson W Good, Peter van Dam, Dana H Brooks, Rob S MacLeod, A cardiac shape model for segmentation uncertainty quantification, in: 2021 Computing in Cardiology (CinC)

5. Jess D Tate, Wilson W Good, Nejib Zemzemi, Machteld Boonstra, Peter van Dam, Dana H Brooks, Akil Narayan, Rob S MacLeod, Uncertainty quantification of the effects of segmentation variability in ecgi, in: international Conference on Functional Imaging and Modeling of the Heart 2021 (FIMH 2021)

6. Ghimire S, Dhamala J, Coll-Font J, Tate JD, Guillem MS, Brooks DH, MacLeod RS, Wang L. Overcoming barriers to quantification and comparison of electrocardiographic imaging methods: A community-based approach. In: 2017 Computing in Cardiology (CinC); 2017 Sep 24 (pp. 1-4). IEEE. https://doi.org/10.22489/CinC.2017.370-289

7. Tate J, Gillette K, Burton B, Good W, Zenger B, Coll-Font J, Brooks D, MacLeod R. Reducing error in ECG forward simulations with improved source sampling. Frontiers in physiology. 2018 Sep 21;9:1304. https://doi.org/10.3389/fphys.2018.01304

8. Tate JD, Zemzemi N, Good WW, van Dam P, Brooks DH, MacLeod RS. Effect of segmentation variation on ECG imaging. In: 2018 Computing in Cardiology Conference (CinC); 2018 Sep 23 (Vol. 45, pp. 1-4). IEEE. https://doi.org/10.22489/CinC.2018.374

9. Tate JD, Zemzemi N, Good WW, van Dam P, Brooks DH, MacLeod RS. Shape analysis of segmentation variability. In 2020 Computing in Cardiology 2020 Sep 13 (pp. 1-4). IEEE.

10. Tate JD, Good WW, Zemzemi N, Boonstra M, van Dam P, Brooks DH, Narayan A, MacLeod RS. Uncertainty Quantification of the Effects of Segmentation Variability in ECGI. InInternational Conference on Functional Imaging and Modeling of the Heart 2021 Jun 21 (pp. 515-522). Springer, Cham.

11. Tate JD, Elhabian S, Zemzemi N, Good WW, van Dam P, Brooks DH, MacLeod RS.  A Cardiac Shape Model for Segmentation Uncertainty Quantification. In 2021 Computing in Cardiology, 2021 Sep 13.