Automatic localization of anatomical landmarks in cardiac MR perfusion using random forests

Abstract Automatic localization of anatomical landmarks in myocardial perfusion magnetic resonance (MR) data is considered to be a preliminary step toward fully automatic quantification of regional upslope or blood flow in the myocardium. The goal of this work is to develop an automatic method based on supervised learning and detect anatomical landmarks with high accuracy from myocardial perfusion MR data. Dynamic contrast enhanced myocardial MR perfusion data were acquired using a standard perfusion sequence. To effectively extract characteristic features for left ventricle (LV) center point and anterior right ventricle (RV) insertion point, we performed feature extraction of the two landmarks independently from an LV enhancement frame and an RV enhancement frame. Feature extraction of pixel intensity, Sobel gradient, and Haar-like features was performed. Cross-validation from training data was used to build a random forests classifier. We used 38 subjects’ data as training datasets and 21 subjects’ data as test datasets. The proposed method provided high accuracy in localization of the LV center point and anterior RV insertion point. The mean (±SD) localization errors of the proposed method were 3.38 (±2.36) mm for the LV center point and 4.23 (±1.97) mm for the anterior RV insertion point. The proposed method shows the potential to automatically localize anatomical landmarks for the segmental analysis of myocardial perfusion in MRI.