Computer Science Location: Rice 108
Add to Calendar 2018-04-19T11:00:00 2018-04-19T13:00:00 America/New_York Master's Thesis Presentation by Anudeep Konda Automatic Quantification of Cardiac MRI for Hypertrophic Cardiomyopathy Rice 108

Automatic Quantification of Cardiac MRI for Hypertrophic Cardiomyopathy

Abstract: Hypertrophic cardiomyopathy (HCM) is the most common monogenic heart disease, characterized by unexplained left ventricular hypertrophy, myofibrillar disarray and myocardial fibrosis. Left and right ventricular mass, ejection fraction and myocardium wall thickness at different segments measured from cardiac cine MRI based on LV and RV segmentation are critical biomarkers for diagnosis and prognosis of HCM patients. Deep convolutional neural networks (DCNNs) have shown great promise in many medical image segmentation tasks, including cardiac MRI. However, due to the greatly increased variability in shape and size of heart chambers and often reduced image contrast, the segmentation for HCM is more challenging than healthy and other patient populations and the model trained on generic cardiac MRI is very likely to fail on HCM. In this study, we developed a cascaded deep convolutional neural network to automatically segment the epi and endocardium at end-diastole and end-systole phases and calculate all variables of interest based on a database with 100 HCM patients. Ejection fraction, LV and RV mass, and regional wall thickness at 6 automatically localized segments per slice were also calculated with promising results. The model greatly reduces the post-processing time for biomarker quantifications for HCM patients.

Committee Members: Vicente Ordonez-Roman (Advisor), Craig H. Meyer (Chair), Nada Basit, Xue Feng