Name
Abstract | ||
|---|---|---|
Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and were 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. We report state-of-the-art results in 8 out of 10 evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1038/s41598-018-24304-3 | SCIENTIFIC REPORTS |
Field | DocType | Volume |
Spinal cord,Neuroscience,Network parameter,Segmentation,Medical imaging,Amyotrophic lateral sclerosis,Medicine,Spinal cord gray matter | Journal | 8 |
Issue | ISSN | Citations |
1 | 2045-2322 | 6 |
PageRank | References | Authors |
0.55 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian S. Perone | 1 | 33 | 3.20 |
| Evan Calabrese | 2 | 78 | 5.50 |
| Julien Cohen-Adad | 3 | 472 | 29.21 |