Atrial Segmentation using Deep Learning

Abstract

Measurements of the left atrium are key indicators for aprior prediction of heart disease. The intricate and complex nature of cardiac anatomy necessitates precise segmentation. This work presents a deep learning based methodology for left atrial segmentation in cardiac magnetic resonance imaging (MRI) for reliable automatic segmentation of the left atrium apendage. Inspired by recent advancements in medical image analysis, the approach proposes an architecture designed to enhance prediction accuracy. The results are evaluated using standard performance metrics, showcasing effectiveness and reliabil ity of the technique. These results not only demonstrate remarkable segmentation but also provide a foundation for future advancements in cardiac image analysis. This study investigates a nd caters to the unique challenges posed within cardiac MRI data. It contributes to the continual evolution of medical image segmentation, ensuring a heartbeat closer to more accurate and clinically impactful diagnoses.