# Inter-operator segmentation variability induces high premature ventricular contractions localization uncertainty at the heart base

Narimane Gassa, Jess Tate, Machteld Boonstra, Beata Ondrusova, Jana Svehlikova, Rob Macleod, Nejib Zemzemi

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## To cite this version:

Narimane Gassa, Jess Tate, Machteld Boonstra, Beata Ondrusova, Jana Svehlikova, et al.. Inter-operator segmentation variability induces high premature ventricular contractions localization uncertainty at the heart base. Heart Rhythm 2023 - The Society's 44th annual meeting, May 2023, New Orleans (LA), United States. 2023. [hal-04124313](https://hal.science/hal-04124313)

HAL Id: hal-04124313

https://hal.science/hal-04124313

Submitted on 13 Oct 2023

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# Inter-Operator Segmentation Variability Induces High Premature Ventricular Contractions Localization Uncertainty at the Heart Base

Narimane Gassa¹, Jess Tate², Machteld Boonstra³, Beata Ondrusova⁴, Jana Svehlikova⁴, Rob MacLeod², Nejib Zemzemi¹

¹ Centre Inria de l'Université de Bordeaux, Talence, France – ² University of Utah, Salt Lake City, United States – ³ University Medical Center Utrecht, Almere, Netherlands – ⁴ Institute of Measurement Science, Slovak Academy of Sciences.

## Background

Electrocardiographic imaging (ECGI) has become a valuable non-invasive technique for diagnosing and treating cardiac arrhythmia. It involves using body surface potentials and anatomical models with specific electrode positions to estimate the heart's electrical activity. However, solving the inverse problem in ECGI requires an initial step of image segmentation and mesh generation. It's crucial for accurate ECGI but its success may be dependent on the operator's expertise and the segmentation method.

## Objective

Our main purpose is to evaluate the effect of the inter-operator segmentation variability on the PVC localization.

## Method

* For a singular subject, the consortium of ECGI received 10 distinct cardiac segmentations from operators in different institutions, with the purpose of examining the impact of segmentation.

<figure><img src="image_3.png" /><figcaption>Figure 1. Original segmentations</figcaption></figure>

* In this study, we solve the inverse problem in electrocardiography to find the source activation given the body surface potentials (BSPs) twice. For the first test case, we solve ECGI for each cardiac segmentation and its corresponding BSP. In the second test case, we use one geometry and its corresponding BSP as a reference to solve ECGI for all geometries.

* We used eight stimulation protocols distributed as follows: left and right ventricular free walls (LV, RV), apex, left and right ventricular outflow tract (LVOT, RVOT), septum, and two locations at the left and right heart base (LVB, RVB).

<figure><img src="image_4.png" /><figcaption>Figure 2. Stimulation sites</figcaption></figure>

## Results

We analyze quantitatively the segmentation variability and compute the Dice coefficient between each pair of segmentation (Figure 3).

<figure><img src="image_5.png" /><figcaption>Figure 3. Dice coefficient between each pair of segmentation</figcaption></figure>

We perform reconstruction of the heart surface potentials and the corresponding activation maps for each solution and stimulation protocol. Using these activation maps, we identify the earliest activation sites (EASs).

<figure><img src="image_6.png" /><figcaption>Figure 4. Distribution of ECGI based EASs with and without geometry uncertainty for an RVB stimulation.</figcaption></figure>

## Conclusion

* The localization of PVC can be significantly affected by the variability in segmentation, particularly in the RVOT, LVOT, RVB, and LVB. Our analysis suggests that the increased uncertainty in source detection can be attributed to the greater variability in segmentation at the base of the heart

* It is important to note that variability in cardiac segmentation can affect the interpretability of ECGI in clinical applications. Therefore, we highly recommend conducting segmentation with utmost care, particularly at the base of the heart.

<figure><img src="image_7.png" /><figcaption>Figure 5. Box plots of the distribution of Localization Error (LE), in millimeter, across the different pacing sites.</figcaption></figure>

<table><caption>Table 1. Average LEs obtained with (first row) and without (second row) geometry uncertainty.</caption><thead><tr><th></th><th>LV</th><th>RV</th><th>Septal</th><th>Apex</th><th>LVB</th><th>RVOT</th><th>LVOT</th><th>RVB</th></tr></thead><tbody><tr><td>Test A : No geometry uncertainty</td><td>12</td><td>10</td><td>38</td><td>13</td><td>24</td><td>14</td><td>23</td><td>15</td></tr><tr><td>Test B : With geometry uncertainty</td><td>14</td><td>10</td><td>41</td><td>14</td><td>25</td><td>15</td><td>27</td><td>19</td></tr></tbody></table>

## Disclosures / Acknowledgments

This Project has received funding from the European Unions Horizon research and innovation programme under the Marie Skodowska-Curie grant agreement No.860974

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Université
de BORDEAUX

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