Name
Abstract | ||
|---|---|---|
The thrombus causing a stroke can be seen on the susceptibility weighted angiography (SWAN) magnetic resonance imaging (MRI) sequence. But it is very small and hard to detect by humans. Up to date the thrombus is identified by trained human experts. But as stroke needs quick treatment, an automatic detection of the thrombus would be useful to speed up the diagnosis of acute stroke. We propose a method for automatic thrombus detection from SWAN using three separate U-Nets which work on the axial, coronal and sagittal planes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IPTA.2019.8936074 | 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA) |
Keywords | Field | DocType |
Stroke,Thrombus,Deep Learning,MRI,Automatic Segmentation. | Computer vision,Thrombus,Coronal plane,Segmentation,Computer science,Stroke,Artificial intelligence,Sagittal plane,Multi directional,Angiography,Magnetic resonance imaging | Conference |
ISSN | ISBN | Citations |
2154-5111 | 978-1-7281-3976-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Kobold | 1 | 0 | 0.34 |
| Elmar Wolfgang Lang | 2 | 260 | 36.10 |
| Ana Maria Tomé | 3 | 0 | 0.34 |
| Vincent Vigneron | 4 | 20 | 4.64 |
| Hichem Maaref | 5 | 0 | 0.34 |
| D. Fourer | 6 | 0 | 0.34 |
| M. Aghasaryan | 7 | 0 | 0.34 |
| C. Alecu | 8 | 0 | 0.34 |
| N. Chausson | 9 | 0 | 0.34 |
| Y. L'Hermitte | 10 | 0 | 0.34 |
| D. Smadja | 11 | 0 | 0.34 |