Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections

Segmentation of grey matter in magnetic resonance images of the spinal cord is an important step in assessing disease state in neurological disorders such as multiple sclerosis. However, manual delineation of spinal cord tissue is time-consuming and susceptible to variability introduced by the rater. We present a novel segmentation method for spinal cord tissue that uses fully convolutional encoder networks (CENs) for direct end-to-end training and includes shortcut connections to combine multi-scale features, similar to a u-net. While CENs with shortcuts have been used successfully for brain tissue segmentation, spinal cord images have very different features, and therefore deserve their own investigation. In particular, we develop the methodology by evaluating the impact of the number of layers, filter sizes, and shortcuts on segmentation accuracy in standard-resolution cord MRIs. This deep learning-based method is trained on data from a recent public challenge, consisting of 40 MRIs from 4 unique scan sites, with each MRI having 4 manual segmentations from 4 expert raters, resulting in a total of 160 image-label pairs. Performance of the method is evaluated using an independent test set of 40 scans and compared against the challenge results. Using a comprehensive suite of performance metrics, including the Dice similarity coefficient (DSC) and Jaccard index, we found shortcuts to have the strongest impact (0.60 to 0.80 in DSC), while filter size (0.76 to 0.80) and the number of layers (0.77 to 0.80) are also important considerations. Overall, the method is highly competitive with other state-of-the-art methods.