EchoSAM: SAM adaption for unified 2D echocardiography segmentation and ejection fraction calculation

Abstract

Automatic segmentation of echocardiography and the calculation of clinical data play a crucial role in the assessment of cardiac function. The left ventricular ejection fraction (LVEF) is a key indicator of the heart’s systolic performance. In this study, we present EchoSAM, a unified framework designed for integrated structures segmentation and LVEF calculation based on the Segment Anything Model (SAM). SAM exhibits strong and precise zero-shot segmentation skills in natural images. Nevertheless, because of the domain difference, it cannot be applied to echocardiography. In order to mitigate the effects of blurred boundaries, low contrast, and high noise, we enhance the Image Encoder to acquire more informative features. Meanwhile, according to the LVEF calculation of Simpson method, we design a points localization module, leveraging the combination of image features and Mask Decoder output to obtain precise points locations. Our EchoSAM model not only enables an accurate LVEF calculation in a fully automatic way, but also allows checking the segmentation quality of cardiac structures to ensure clear and reliable clinical analysis. We rigorously evaluated our approach on three datasets: CAMUS, EchoNet-Dynamic, and EchoDUT. The results demonstrate that EchoSAM has achieved Dice of 94.03% for the left ventricle (LV), 88.48% for the myocardium (MYO), superior to other state-of-the-art methods. Additionally, LVEF yielded a mean absolute error (MAE) of 6.39% and a root mean square error (RMSE) of 8.56%.