MultiEchoNet achieved high accuracy in estimating left ventricular ejection fraction and mitral annulus diameter, with correlation coefficients of 0.845 and 0.971, respectively.
Does MultiEchoNet accurately automate left ventricular segmentation and calculate LVEF and mitral annulus diameter from echocardiographic sequences?
MultiEchoNet provides an automated, multi-task deep learning approach for accurate left ventricular segmentation, keypoint localization, and calculation of LVEF and mitral annulus diameter from echocardiograms.
Absolute Event Rate: 0% vs 0%
Quantification of left ventricular function is essential for diagnosing cardiovascular diseases. Current clinical practice requires interactive segmentation of ultrasound images to delineate the left ventricular region and identify keypoints such as the apex and mitral annulus, a process that is both time-consuming and inefficient. To address these limitations, we introduce MultiEchoNet, a multi-task network employing a weakly supervised learning strategy to automatically calculate the left ventricular ejection fraction (LVEF) and mitral annulus diameter (MAD). Our approach integrates a novel task propagation module designed to improve the network's ability to capture global semantic information for each task at reduced computational cost, thereby minimizing task interference and enhancing task-specific feature extraction. Furthermore, we developed a multi-task Transformer module to facilitate the extraction of complementary modality information across tasks, promoting mutual guidance and optimization. This enables concurrent left ventricular segmentation and keypoint localization. In addition, peak detection is utilized to identify the end-systolic frame and end-diastolic frame in the echocardiographic sequence generated by the network, allowing for the precise calculation of related parameters. Experimental evaluations on public datasets EchoNet-Dynamic and CAMUS demonstrate that our algorithm achieves Dice similarity coefficients of 93.51% and 93.18%, respectively, and the highest keypoint similarity scores were 0.958 and 0.940, respectively. Additionally, the correlation coefficients between the predicted and true LVEF values were 0.845 and 0.82, respectively, while those for MAD were 0.971 and 0.963, respectively. These results suggest that MultiEchoNet offers robust support for the auxiliary diagnosis of cardiovascular diseases. Code is available at https://github.com/zzzmmmlll965/MultiEchoNet .
Zhou et al. (Mon,) reported a other. MultiEchoNet achieved high accuracy in estimating left ventricular ejection fraction and mitral annulus diameter, with correlation coefficients of 0.845 and 0.971, respectively.