Analytics pipeline for left ventricle segmentation and volume estimation on cardiac MRI using deep learning

The left ventricle (LV) is the largest chamber in the heart and plays a critical role in cardiac function. Noninvasive cardiac imaging modalities (e.g., cardiac magnetic resonance (CMR), transesophageal echocardiography (TEE), and computed tomography (CT)) are commonly used to study LV size and function in addition to other cardiac structural aspects such as valvular disease, and are invaluable tools for the diagnosis and management of heart disease. However, the process of analyzing cardiac images is time-consuming and labor-intensive. Automatic LV segmentation and volume estimation from cardiac images are thus essential in providing efficient and consistent analysis. We discuss findings from our investigation into different techniques for processing and analyzing CMR images and present the methods giving best performance in an end -to -end analytics pipeline for LV segmentation and volume estimation. This pipeline can serve as an initial step towards analyzing CMR at scale to aid in non-invasive cardiac disease detection.