3. The method of claim 1 , wherein extracting a plurality of 1D profiles along each of the plurality of overlapping fixed size branch segments comprises: extracting, for each of the plurality of overlapping fixed length branch segments, 1D profiles of vessel scale, image intensity, centerline curvature, tubularity measure, image intensity and gradient statistics along and inside a cross-sectional circular boundary, and distance to a most proximal point in the branch of the detected centerline tree.
1. A method for machine learning based classification of vascular branches to distinguish falsely detected branches from true branches, comprising: sampling a plurality of overlapping fixed size branch segments from one or more branches of a detected centerline tree of a target vessel extracted from a medical image of a patient; extracting a plurality of 1D profiles along each of the plurality of overlapping fixed size branch segments; calculating a probability score for each of the plurality of overlapping fixed size branch segments based on the plurality of 1D profiles using a trained deep neural network classifier; assigning a final probability score to each of a plurality of centerline points in the one or more branches of the detected centerline tree of the target vessel based on the probability scores of the overlapping fixed size branch segments containing that centerline point; and pruning the one or more branches of the detected centerline tree of the target vessel based on the final probability scores of the plurality of centerline points in the one or more branches of the detected centerline tree of the target vessel to remove falsely detected branches from true branches in the one or more branches of the detected centerline tree of the target vessel.