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.
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. 7. The method of claim 1 , wherein 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 comprises: for each of the plurality of centerline points in the one or more branches of the detected centerline tree of the target vessel: determining a maximum probability score among the probability scores of the overlapping fixed size branch segments containing that centerline point, and assigning the maximum probability score among the probability scores of the overlapping fixed size branch segments containing that centerline point as the final probability score of the centerline point.