1. An enhanced auto-segmentation method comprising: performing, with a processor, atlas-based auto-segmentation on a plurality of points in a subject image using atlas images to generate first data representative of at least one structure in the subject image, wherein performing the atlas-based auto-segmentation includes registering the subject image with the atlas images to map points of the subject image to points of the atlas images; applying, with the processor, a plurality of points in the subject image to a trained classifier to generate second data representative of the at least one structure in the subject image; combining, with the processor, the first data with the second data to generate third data representative of the at least one structure in the subject image; and determining, based on the third data, structure classifications associated with the subject image.
1. An enhanced auto-segmentation method comprising: performing, with a processor, atlas-based auto-segmentation on a plurality of points in a subject image using atlas images to generate first data representative of at least one structure in the subject image, wherein performing the atlas-based auto-segmentation includes registering the subject image with the atlas images to map points of the subject image to points of the atlas images; applying, with the processor, a plurality of points in the subject image to a trained classifier to generate second data representative of the at least one structure in the subject image; combining, with the processor, the first data with the second data to generate third data representative of the at least one structure in the subject image; and determining, based on the third data, structure classifications associated with the subject image. 12. The method of claim 1 , wherein the trained classifier was trained using a random forests machine learning algorithm, the trained classifier comprises a plurality of decision trees configured to process the applied subject image points, each decision tree is organized as a plurality of branches and a plurality of nodes to implement a plurality of decision rules, the nodes comprise a plurality of internal nodes and a plurality of terminal nodes, each internal node is configured to test an attribute of an applied image point, each branch corresponds to an outcome of the test, and each terminal node holds a structure label for association with an applied subject image point.