Automatic left ventricular myocardium segmentation and volumetric classification of scar
- MPhil CS 2018-2019
Cardiac function is of predominant importance for both prognosis and treatment of different pathologies such as mitral regurgitation,ischmia,dyssynchrony and myocarditis. Cardiac behaviour is determined by structural and functional features. In both cases, the analysis of medical imaging studies requires to detect and segment the myocardium. Nowadays, magnetic resonance imaging is one of the most relevant and accurate non-invasive diagnostic tools for cardiac structure and function. In this, we present deep learning technique stack auto encoder architecture to segment images from a series of short-axis cardiac magnetic resonance slices, we propose to tackle the problem of automated left ventricle segmentation through the application of deep learning technique.It takes as input raw magnetic resonance imaging images,requires no manual pre processing or image cropping and is trained to segment the endocardium of the left ventricle, as well as the center of the left ventricle. The quantitative evaluation is based on three commaon metrics which are accuracy, dice and sensitivity respectively and classifciation using random forest classifier. The classifciation is to be done on the image set to determine whether the myocardium is viable or normal.