Accurate localization, identification and segmentation of vertebrae
is an important task in medical as well as biological image analysis.
The prevailing approach to solve such a task is to first generate
pixel-independent features for each vertebra, e.g. via a random forest
predictor, which are then fed into an MRF-based objective to infer the
optimal MAP solution of a constellation model. We abandon this static,
two-stage approach and mix feature generation with model-based inference
in a new, more flexible, way. We evaluate our method on two data
sets with different objectives. The first is semantic segmentation of a 21-
part developing spine of zebrafish in microscopy images, and the second
is localization and identification of vertebrae in benchmark human CT.