Ventricle tracking in transesophageal echocardiography (TEE) images during cardiopulmonary resuscitation (CPR) using deep learning and monogenic filtering.

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-023-00293-9.

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

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.