21. A non-transitory computer readable medium storing computer program instructions for machine learning based classification of vascular branches to distinguish falsely detected branches from true branches, the computer program instructions when executed by a processor cause the processor to perform operations 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.
21. A non-transitory computer readable medium storing computer program instructions for machine learning based classification of vascular branches to distinguish falsely detected branches from true branches, the computer program instructions when executed by a processor cause the processor to perform operations 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. 27. The non-transitory computer readable medium of claim 21 , wherein 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 comprises: classifying each of the plurality of centerline points in the one or more branches of the detected centerline tree of the target vessel as a vessel point or a non-vessel point based on the final probability scores of the plurality of centerline points; and pruning the one or more branches of the detected centerline tree of the target vessel based on the classification of each of the plurality of centerline points as a vessel point or a non-vessel point.