  Intervertebral discs (IVDs), as small joints lying between adjacent
vertebrae, have played an important role in pressure buffering and tissue
protection. The fully-automatic localization and segmentation of IVDs have been
discussed in the literature for many years since they are crucial to spine
disease diagnosis and provide quantitative parameters in the treatment.
Traditionally hand-crafted features are derived based on image intensities and
shape priors to localize and segment IVDs. With the advance of deep learning,
various neural network models have gained great success in image analysis
including the recognition of intervertebral discs. Particularly, U-Net stands
out among other approaches due to its outstanding performance on biomedical
images with a relatively small set of training data. This paper proposes a
novel convolutional framework based on 3D U-Net to segment IVDs from
multi-modality MRI images. We first localize the centers of intervertebral
discs in each spine sample and then train the network based on the cropped
small volumes centered at the localized intervertebral discs. A detailed
comprehensive analysis of the results using various combinations of
multi-modalities is presented. Furthermore, experiments conducted on 2D and 3D
U-Nets with augmented and non-augmented datasets are demonstrated and compared
in terms of Dice coefficient and Hausdorff distance. Our method has proved to
be effective with a mean segmentation Dice coefficient of 89.0% and a standard
deviation of 1.4%.
