  Segmentation, i.e., the partitioning of volumetric data into components, is a
crucial task in many image processing applications ever since such data could
be generated. Most existing applications nowadays, specifically CNNs, make use
of voxelwise classification systems which need to be trained on a large number
of annotated training volumes. However, in many practical applications such
data sets are seldom available and the generation of annotations is
time-consuming and cumbersome. In this paper, we introduce a novel voxelwise
segmentation method based on active learning on geometric features. Our method
uses interactively provided seed points to train a voxelwise classifier based
entirely on local information. The combination of an ad hoc incorporation of
domain knowledge and local processing results in a flexible yet efficient
segmentation method that is applicable to three-dimensional volumes without
size restrictions. We illustrate the potential and flexibility of our approach
by applying it to selected computed tomography scans where we perform different
segmentation tasks to scans from different domains and of different sizes.
