  The vertebral levels of the spine provide a useful coordinate system when
making measurements of plaque, muscle, fat, and bone mineral density. Correctly
classifying vertebral levels with high accuracy is challenging due to the
similar appearance of each vertebra, the curvature of the spine, and the
possibility of anomalies such as fractured vertebrae, implants, lumbarization
of the sacrum, and sacralization of L5. The goal of this work is to develop a
system that can accurately and robustly identify the L1 level in large
heterogeneous datasets. The first approach we study is using a 3D U-Net to
segment the L1 vertebra directly using the entire scan volume to provide
context. We also tested models for two class segmentation of L1 and T12 and a
three class segmentation of L1, T12 and the rib attached to T12. By increasing
the number of training examples to 249 scans using pseudo-segmentations from an
in-house segmentation tool we were able to achieve 98% accuracy with respect to
identifying the L1 vertebra, with an average error of 4.5 mm in the
craniocaudal level. We next developed an algorithm which performs iterative
instance segmentation and classification of the entire spine with a 3D U-Net.
We found the instance based approach was able to yield better segmentations of
nearly the entire spine, but had lower classification accuracy for L1.
