ESPNet: An Automated Left Ventricular Ejection Fraction Measurement via Multi-Frame Temporal Aggregation.
TL;DR
The results demonstrate that ESPNet not only outperforms existing methods but also achieves a level of consistency comparable to inter observer variability among mid-career clinicians, effectively addressing the clinical need for reliable and efficient automated LVEF measurement.
Abstract
Left ventricular ejection fraction (LVEF) is a key indicator of cardiac function assessed via echocardiography. The biplane Simpson's method for manual measurement is time-consuming and subjective, requiring identification of apical four-chamber (A4C) and two-chamber (A2C) views at end-diastolic (ED) and end-systolic (ES) frames. Automated methods often struggle with consistency and reliability, particularly in heart failure, arrhythmia and low signal-to-noise ratio (SNR) conditions. To address these challenges, this study presents EchoSegPhaseNet (ESP Net), an innovative automated approach for LVEF measurement using multi-frame temporal aggregation. ESP Net comprises two main components: the PhaseFormer module for cardiac phase detection and the LVFormer for Endo segmentation. PhaseFormer significantly improves ED/ES frame prediction accuracy, primarily due to the Multi Probability Prediction Head (MPP), which enables long range temporal receptive fields and effectively aggregates deep-layer features over various distances in the echocar diographic video stream. Following this, LVFormer aggregates deep temporal features from frames around ED/ES, combining spatial-temporal features from one complete respective cardiac cycle of both A2C and A4C views to overcome single-view limitations and significantly enhance segmentation accuracy. This upstream-downstream pro cessing pipeline significantly boosts data processing efficiency. In evaluations on both private and public datasets, ESPNet showcases a faster inference speed in phase detection compared to existing methods, while maintaining similar accuracy levels. In the Endo segmentation task, it not only achieves superior speed but also outperforms current methods in accuracy, with a Pearson correlation coefficient of 0.981, an R2 of 0.96, and a mean absolute error of 3.281 between automated LVEF measurements and manual physician calculations. These results demonstrate that ESPNet not only outperforms existing methods but also achieves a level of consistency comparable to inter observer variability among mid-career clinicians, effectively addressing the clinical need for reliable and efficient automated LVEF measurement.