Spinal cord gray matter segmentation using deep dilated convolutions
This work addresses the need for automated segmentation to study neurological disorders like amyotrophic lateral sclerosis, though it appears incremental as it builds on existing deep learning approaches.
The authors tackled the problem of automatically segmenting spinal cord gray matter from MRI scans, achieving state-of-the-art results in 8 out of 10 evaluation metrics and reducing network parameters compared to traditional methods like U-Nets.
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.