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

다국어 초록 (Multilingual Abstract)

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 clearechocardiographic images without interfering with CPR. The quality of CPR would be optimized if the myocardial ejectionfraction (EF) could be calculated in real-time during CPR. We conducted a study to derive a protocol to detect systole anddiastole automatically and calculate EF using TEE images acquired from patients with cardiac arrest. The data were supplementedusing thin-plate spline transformation to solve the problem of insufficient data. The deep learning model wasconstructed based on ResUNet + + , and a monogenic filtering method was applied to clarify the ventricular boundary. Theperformance of the model to which the monogenic filter was added and the existing model was compared. The left ventriclewas segmented in the ME LAX view, and the left and right ventricles were segmented in the ME four-chamber view. Inmost of the results, the performance of the model to which the monogenic filter was added was high, and the difference wasvery small in some cases; but the performance of the existing model was high. Through this learned model, the effect ofCPR can be quantitatively analyzed by segmenting the ventricle and quantitatively analyzing the degree of contraction ofthe ventricle during systole and diastole.