000			01890nam a22002057a 4500
003			OSt
005			20220107122853.0
008			190710b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aJayadeep (41718004) _916160
245			_aAutomatic left ventricular myocardium segmentation and volumetric classification of scar
300			_aMPhil CS 2018-2019
500			_aCardiac 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.
502			_bMPhil CS _c2018-2019 _dINT _eDr Elizabeth Sherly
650			_aVENTRICULAR MYOCARDIUM _916161
650			_aCARDIAC MRI _916162
650			_aCMR IMAGES _916163
650			_aCONVOLUTIONAL NEURAL NETWORK _916164
942			_2ddc _cPR
999			_c6567 _d6567