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KMID : 1144120230130040715
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Biomedical Engineering Letters
2023 Volume.13 No. 4 p.715 ~ p.728
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Ventricle tracking in transesophageal echocardiography (TEE) images during cardiopulmonary resuscitation (CPR) using deep learning and monogenic filtering
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Ahn Hong-Gi
Kim Sun-Ju
Kang Seung-Young
Han Jung-Hun
Hwang Sung-Oh
Cha Kyoung-Chul
Yang Se-Jung
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Abstract
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High-quality cardiopulmonary resuscitation (CPR) is the most important factor in promoting resuscitation outcomes; therefore, monitoring the quality of CPR is strongly recommended in current CPR guidelines. Recently, transesophageal echocardiography (TEE) has been proposed as a potential real-time feedback modality because physicians can obtain clear echocardiographic images without interfering with CPR. The quality of CPR would be optimized if the myocardial ejection fraction (EF) could be calculated in real-time during CPR. We conducted a study to derive a protocol to detect systole and diastole automatically and calculate EF using TEE images acquired from patients with cardiac arrest. The data were supplemented using thin-plate spline transformation to solve the problem of insufficient data. The deep learning model was constructed based on ResUNet?+??+?, and a monogenic filtering method was applied to clarify the ventricular boundary. The performance of the model to which the monogenic filter was added and the existing model was compared. The left ventricle was segmented in the ME LAX view, and the left and right ventricles were segmented in the ME four-chamber view. In most of the results, the performance of the model to which the monogenic filter was added was high, and the difference was very small in some cases; but the performance of the existing model was high. Through this learned model, the effect of CPR can be quantitatively analyzed by segmenting the ventricle and quantitatively analyzing the degree of contraction of the ventricle during systole and diastole.
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KEYWORD
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Cardiopulmonary resuscitation (CPR), Transesophageal echocardiography (TEE), Ejection fraction (EF), Deep learning (DL)
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