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Spinal cord gray matter segmentation using deep dilated convolutions
Gray matter (GM) tissue changes have been associated with a wide range of
neurological disorders and was also recently found relevant as a biomarker for
disability in amyotrophic lateral sclerosis. The ability to automatically
segment the GM is, therefore, an important task for modern studies of the
spinal cord. In this work, we devise a modern, simple and end-to-end fully
automated human spinal cord gray matter segmentation method using Deep
Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate
our method against six independently developed methods on a GM segmentation
challenge and report state-of-the-art results in 8 out of 10 different
evaluation metrics as well as major network parameter reduction when compared
to the traditional medical imaging architectures such as U-Nets.Comment: 13 pages, 8 figure
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