Dataset Information

Left ventricle segmentation in transesophageal echocardiography images using a deep neural network.

ABSTRACT:

Purpose

There has been little progress in research on the best anatomical position for effective chest compressions and cardiac function during cardiopulmonary resuscitation (CPR). This study aimed to divide the left ventricle (LV) into segments to determine the best position for effective chest compressions using the LV systolic function seen during CPR.

Methods

We used transesophageal echocardiography images acquired during CPR. A deep neural network with an attention mechanism and a residual feature aggregation module were applied to the images to segment the LV. The results were compared between the proposed model and U-Net.

Results

The results of the proposed model showed higher performance in most metrics when compared to U-Net: dice coefficient (0.899±0.017 vs. 0.792±0.027, p<0.05); intersection of union (0.822±0.026 vs. 0.668±0.034, p<0.05); recall (0.904±0.023 vs. 0.757±0.037, p<0.05); precision (0.901±0.021 vs. 0.859±0.034, p>0.05). There was a significant difference between the proposed model and U-Net.

Conclusion

Compared to U-Net, the proposed model showed better performance for all metrics. This model would allow us to evaluate the systolic function of the heart during CPR in greater detail by segmenting the LV more accurately.

SUBMITTER: Kang S

PROVIDER: S-EPMC9858054 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Left ventricle segmentation in transesophageal echocardiography images using a deep neural network.

Kang Seungyoung S Kim Sun Ju SJ Ahn Hong Gi HG Cha Kyoung-Chul KC Yang Sejung S

PloS one 20230120 1

<h4>Purpose</h4>There has been little progress in research on the best anatomical position for effective chest compressions and cardiac function during cardiopulmonary resuscitation (CPR). This study aimed to divide the left ventricle (LV) into segments to determine the best position for effective chest compressions using the LV systolic function seen during CPR.<h4>Methods</h4>We used transesophageal echocardiography images acquired during CPR. A deep neural network with an attention mechanism ...[more]

PMID: 36662773

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Dataset Information

Left ventricle segmentation in transesophageal echocardiography images using a deep neural network.

Purpose

Methods

Results

Conclusion

Publications

Left ventricle segmentation in transesophageal echocardiography images using a deep neural network.

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