Cardiac MRI Segmentation Using Efficient ResNeXT-50-Based IEI Level Set and Anisotropic Sigmoid Diffusion Algorithms

Abstract

Endocardial and epicardial border identification has been of extensive interest in cardiac Magnetic Resonance Images (MRIs). It is a difficult job to segment the epicardium and endocardium accurately and automatically from cardiac MRI owing to the cardiac tissues’ complexity even though the prevailing Deep Learning (DL) methodologies had attained significant success in medical imaging segmentation. Hence, by employing effectual ResNeXT-50-centric Inverse Edge Indicator Level Set (IEILS) and anisotropic sigmoid diffusion algorithms, this system has proposed cardiac MRI segmentation. The work has endured some function for an effectual partition of epicardium and endocardium. Initially, by employing the Truncated Kernel Function (TK)-Trilateral Filter, the noise removal function is executed on the input cardiac MRI. Next, by wielding the ResNeXT-50 IEILS, the Left and Right Ventricular (LV/RV) regions are segmented. The epicardium and endocardium are segmented by the ASD algorithm once the LV/RV is separated from the Left Ventricle (LV) region. Here, the openly accessible Sunnybrook and the Right Ventricle (RV) datasets are wielded. Then, the prevailing state-of-art algorithms are analogized to the outcomes achieved by the proposed framework. Regarding accuracy, sensitivity, and specificity, the proposed methodology executed the cardiac MRI segmentation process precisely along with the other surpassed state-of-the-art methodologies.