A Learning-Free Approach to Whole Spine Vertebra Localization in MRI.

In recent years, analysis of magnetic resonance images of the spine gained considerable interest with vertebra localization being a key step for higher level analysis. Approaches based on trained appearance - which are de facto standard - may be inappropriate for certain tasks, because processing usually takes several minutes or training data is unavailable. Learning-free approaches have yet to show there competitiveness for whole-spine localization. Our work fills this gap. We combine a fast engineered detector with a novel vertebrae appearance similarity concept. The latter can compete with trained appearance, which we show on a data set of 64 (T_1)- and 64 (T_2)-weighted images. Our detection took (27.7 pm 3.78) s with a detection rate of 96.0 % and a distance to ground truth of (3.45 pm 2.2) mm, which is well below the slice thickness.