Name
Title | ||
|---|---|---|
Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization. |
Abstract | ||
|---|---|---|
Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s12880-017-0198-4 | BMC Medical Imaging |
Keywords | Field | DocType |
Brain tumors,MRI,Non-negative matrix factorization,Segmentation,Unsupervised classification | Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Clinical Practice,Brain tumor segmentation,Tumor segmentation,Parametric statistics,Non-negative matrix factorization,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
17 | 1 | 1471-2342 |
Citations | PageRank | References |
1 | 0.35 | 15 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Sauwen | 1 | 1 | 1.70 |
| Marjan Acou | 2 | 1 | 1.02 |
| Diana M Sima | 3 | 8 | 5.89 |
| Jelle Veraart | 4 | 186 | 8.82 |
| Frederik Maes | 5 | 2246 | 273.57 |
| Uwe Himmelreich | 6 | 102 | 9.60 |
| Eric Achten | 7 | 26 | 6.64 |
| Sabine Van Huffel | 8 | 1058 | 149.38 |