Abstract: A process includes: generation of knowledge of the anatomical object to be separated from neighboring objects and identification of high level features (e.g., prior knowledge that includes: a mean shape template of the vertebra; and, high level features, e.g., readily identifiable boundary regions between the vertebrae neighboring objects, such as neighboring vertebra and rib structures); and the subsequent use of this prior knowledge to level set.

Abstract: A computer-implemented method for candidate generation in three-dimensional volumetric data comprises forming a binary volumetric image of the three-dimensional volumetric data including labeled foreground voxels, estimating a plurality of shape features of the labeled foreground voxels in the binary volumetric data including, identifying peak voxels and high curvature voxels from the foreground voxels in the binary volumetric image, accumulating a plurality of confidence values for boundary and each peak voxel, and detecting confidence peaks from the plurality of confidence values, wherein the confidence peaks are determined to be the candidate points, and refining the candidate points given detected confidence peaks, wherein refined candidate points are determined to be candidates.

Abstract: A process includes: generation of knowledge of the anatomical object to be separated from neighboring objects and identification of high level features (e.g., prior knowledge that includes: a mean shape template of the vertebra; and, high level features, e.g., readily identifiable boundary regions between the vertebrae neighboring objects, such as neighboring vertebra and rib structures); and the subsequent use of this prior knowledge to level set.

Abstract: A computer-implemented method for candidate generation in three-dimensional volumetric data comprises forming a binary volumetric image of the three-dimensional volumetric data including labeled foreground voxels, estimating a plurality of shape features of the labeled foreground voxels in the binary volumetric data including, identifying peak voxels and high curvature voxels from the foreground voxels in the binary volumetric image, accumulating a plurality of confidence values for boundary and each peak voxel, and detecting confidence peaks from the plurality of confidence values, wherein the confidence peaks are determined to be the candidate points, and refining the candidate points given detected confidence peaks, wherein refined candidate points are determined to be candidates.