Name
Title | ||
|---|---|---|
A semi-automated image segmentation approach for computational fluid dynamics studies of aortic dissection. |
Abstract | ||
|---|---|---|
Computational studies of aortic hemodynamics require accurate and reproducible segmentation of the aortic tree from whole body, contrast enhanced CT images. Three methods were vetted for segmentation. A semi-automated approach that utilizes denoising, the extended maxima transform, and a minimal amount of manual segmentation was adopted. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6944680 | EMBC |
Keywords | Field | DocType |
computerised tomography,extended maxima transform,semiautomated image segmentation approach,computational fluid dynamics,image segmentation,image denoising,cardiovascular system,aortic hemodynamics,image enhancement,medical image processing,whole body contrast enhanced ct images,aortic dissection,haemodynamics | Computer vision,Scale-space segmentation,Aortic dissection,Computer science,Image segmentation,Artificial intelligence,Computational fluid dynamics | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 1 |
PageRank | References | Authors |
0.36 | 2 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeff R Anderson | 1 | 2 | 1.41 |
| Christof Karmonik | 2 | 16 | 5.74 |
| Yannick Georg | 3 | 1 | 0.36 |
| Jean Bismuth | 4 | 1 | 0.36 |
| Alan B Lumsden | 5 | 1 | 2.05 |
| Adeline Schwein | 6 | 1 | 0.69 |
| Mickael Ohana | 7 | 1 | 0.69 |
| Fabien Thaveau | 8 | 1 | 0.36 |
| Nabil Chakfé | 9 | 1 | 1.03 |