14. An apparatus for machine learning based classification of vascular branches to distinguish falsely detected branches from true branches, comprising: means for 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; means for extracting a plurality of 1D profiles along each of the plurality of overlapping fixed size branch segments; means for 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; means for 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 means for 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.
14. An apparatus for machine learning based classification of vascular branches to distinguish falsely detected branches from true branches, comprising: means for 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; means for extracting a plurality of 1D profiles along each of the plurality of overlapping fixed size branch segments; means for 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; means for 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 means for 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. 16. The apparatus of claim 14 , wherein the probability score calculated for each of the plurality of overlapping fixed size branch segments using the trained deep neural network classifier represents a probability that the overlapping fixed size branch segment is fully part of the target vessel.