  In this paper we introduce the Ladder Algorithm; a novel recurrent algorithm
to detect repetitive structures in natural images with high accuracy using
little training data.
  We then demonstrate the algorithm on the task of extracting vertebrae from
whole spine magnetic resonance scans with only lumbar MR scans for training
data. It is shown to achieve high perforamance with 99.8% precision and recall,
exceeding current state of the art approaches for lumbar vertebrae detection in
T1 and T2 weighted scans. It also generalises without retraining to whole spine
images with minimal drop in accuracy, achieving 99.4% detection rate.
