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The spine is visualized on many CT and MR exams, including thorax and abdomen scans that were originally not intended for spine imaging. Because these often cover several but not all vertebrae, it is difficult to make strong assumptions for automatic analysis. Challenges are therefore the unknown number of target structures (vertebrae) in the image, their anatomical identification (which vertebrae are visible? must not assign the same label to two vertebrae) and that some biomarkers are related only to part of the vertebrae, often the vertebral body. This talk covers an instance segmentation approach for vertebra detection, segmentation, and anatomical identification, and a partitioning approach to separate vertebral body and arch based on thin-plate spline surfaces positioned by a convolutional neural network.