Automated Left Ventricle Segmentation in Cardiac Short-Axis MR Images Using Cost-Volume Filtering and Novel Myocardial Contour Processing Framework

Cardiovascular diseases are often associated with abnormal left ventricular (LV) cardiac parameters, such as deviation of ejection fraction (EF) and cardiac output. These information can be extracted from cardiac magnetic resonance (CMR) scans of the heart, which involves image segmentation in CMR images. Previous works on left ventricle segmentation in CMR images are often hindered by complex inner heart wall geometry or they require a more involved operator intervention. In this work, we employ novel cost-volume filtering (CVF) scheme combined with novel myocardial contour processing framework to overcome the segmentation difficulty resulted from MR imaging artifacts and inner heart wall irregularities (e.g., papillary muscle and trabeculae carneae). Result shows improved accuracy and robustness over previous works. In clinical aspects, quantitative analysis shows close agreement between manually and automatically determined cardiac functions with no systematic bias in EF estimation error.