Ventricular segmentation algorithm for echocardiography based on transfer learning and GAN

Abstract

With the swift progression of computer technology, utilizing deep learning for left ventricular image segmentation in echocardiography is of great significance for automated cardiac function assessment. In this study, a left ventricular segmentation algorithm for echocardiography based on transfer learning and generative adversarial networks (GAN) was proposed. A hierarchical fine-tuning strategy was employed to adaptively adjust network parameters, thereby improving segmentation accuracy. This strategy was based on combining multi-scale GAN and attention mechanisms to enhance segmentation performance, and incorporating the idea of transfer learning into the segmentation network. The algorithm demonstrated efficacy in heart image segmentation tasks, achieving high levels of classification accuracy and producing satisfactory segmentation outcomes. In terms of image segmentation of dual and four chamber images, this method achieved high Dice similarity coefficients and average intersection values. Furthermore, this method demonstrated excellent performance in image segmentation experiments on clinical data, with low average absolute errors in the calculated end diastolic volume, end systolic volume, and ejection fraction. The experimental results demonstrate that the algorithm exhibits satisfactory performance in left ventricular segmentation in echocardiography and is suitable for use in medical clinical data. The study presents a novel approach to ventricular segmentation in echocardiography, which enhances the precision and efficiency of automated cardiac function assessment and has significant clinical applicability.