Name
Abstract | ||
|---|---|---|
The quantitative assessment of neck lymph nodes in the context of malign tumors requires an efficient segmentation technique for lymph nodes in tomographic 3D datasets. We present a Stable 3D Mass-Spring Model for lymph node segmentation in CT datasets. Our model for the first time represents concurrently the characteristic gray value range, directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. Our model design and segmentation accuracy are both evaluated with lymph nodes from clinical CT neck datasets. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.acra.2007.09.001 | Academic Radiology |
Keywords | Field | DocType |
model design,ct,ct datasets,efficient segmentation technique,lymph node segmentation,segmentation accuracy,mass-spring model,segmentation,lymph nodes,clinical ct neck datasets,stable mass-spring models,deformable models,neck lymph node,efficient segmentation process,lymph node | Lymph,Active contour model,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Radiology,Quantitative assessment,Neck lymph nodes,Lymph node | Conference |
Volume | Issue | ISSN |
14 | 11 | Academic Radiology |
ISBN | Citations | PageRank |
3-540-44727-X | 22 | 1.60 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jana Dornheim | 1 | 95 | 13.67 |
| Heiko Seim | 2 | 55 | 6.75 |
| Bernhard Preim | 3 | 1766 | 235.86 |
| Ilka Hertel | 4 | 77 | 7.47 |
| Gero Strauss | 5 | 39 | 5.95 |