  Multi-class segmentation of vertebrae is a non-trivial task mainly due to the
high correlation in the appearance of adjacent vertebrae. Hence, such a task
calls for the consideration of both global and local context. Based on this
motivation, we propose a two-staged approach that, given a computed tomography
dataset of the spine, segments the five lumbar vertebrae and simultaneously
labels them. The first stage employs a multi-layered perceptron performing
non-linear regression for locating the lumbar region using the global context.
The second stage, comprised of a fully-convolutional deep network, exploits the
local context in the localised lumbar region to segment and label the lumbar
vertebrae in one go. Aided with practical data augmentation for training, our
approach is highly generalisable, capable of successfully segmenting both
healthy and abnormal vertebrae (fractured and scoliotic spines). We
consistently achieve an average Dice coefficient of over 90 percent on a
publicly available dataset of the xVertSeg segmentation challenge of MICCAI
2016. This is particularly noteworthy because the xVertSeg dataset is beset
with severe deformities in the form of vertebral fractures and scoliosis.
