Spinal cord gray matter segmentation using deep dilated convolutions

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