Computer Vision News - June 2020

2 Summary RSIP Vision projects 0 Cardiac Magnetic Resonance (CMR) imaging plays a critical role in the assessment and management of patients with coronary artery disease (CAD), a leading cause of death worldwide. However, postprocessing is time-consuming and prone to inter and intraobserver variability. Deep learning neural networks are revolutionizing CMR by automating segmentation and classification tasks, for which RSIP Vision has performed advanced studies and projects. Benefits of Cardiac magnetic resonance: CMR doesn’t require ionizing radiation, as is the case with contrast CT, and its flexibility, high spatial resolution, and 3D capabilities are amenable to assessing myocardial structure and function to identify possible pathologies. The gold standard of cardiac function analysis, CMR gives clinicians insight into ventricular ejection fractions (EF) and stroke volumes (SV), left ventricle mass, and myocardium thickness, however this task requires accurate segmentation of the left ventricular cavity and of the right ventricle for end-diastolic (ED) and end- systolic (ES) phase instances. Challenges: Delineating the left ventricle, myocardium and right ventricle from CMR is very common and necessary to establish a diagnosis. However, fully automatic segmentation has not been perfected, meaning that clinicians must continually perform manual segmentation. This is a time-consuming process that is also prone to observer variability. The success of deep learning in streamlining CMR relies on the automatic performance of two main tasks: 1. Segmentation of the left ventricular endocardium and epicardium and the right ventricular endocardium for both ED and ES phase instances. 2. Classification of the examinations in five classes towards identifying pathologies. The five cases are: normal, heart failure with infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, abnormal right ventricle. Towards addressing this challenge, the ACDC provides a fully-annotated dataset for the purpose of CMR assessment, containing data from 150 multi-equipment CMR recordings acquired in routine clinical practice with manual reference measurements and classification from two medical experts. AI in Cardiac MRI Segmentation