The segmentation of datasets has always been a vital part of many image or volume processing applications, especially regarding tomography data. Common approaches nowadays use either manually tuned methods or rely on techniques like Deep Learning. However, such methods often are useful for segmenting only components of a certainly well-defined type which mostly suffices for clinical data but not for industrial applications anymore. In order to overcome these limitations we propose an interactive approach to volumetric segmentation encompassing robust classifiers and localized volume processing. The resulting algorithm is flexible enough to be used for a broad variety of different segmentation tasks while still generating high quality results. The local processing further enables the segmentation of larger volumes which cannot be handled by existing applications.