1. A computer-implemented method for candidate generation in three-dimensional volumetric data comprising: 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 comprising, identifying peak voxels and high curvature voxels from the foreground voxels in the binary volumetric image, accumulating a plurality of confidence values for each boundary voxel and each peak voxel, 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.