Name
Title | ||
|---|---|---|
A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model. |
Abstract | ||
|---|---|---|
Considering that interventional X-ray imaging systems can have detectors covering an area of about [Formula: see text] ([Formula: see text]) at iso-center, this accuracy is sufficient to facilitate automatic positioning of the X-ray system. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11548-018-1871-y | International journal of computer assisted radiology and surgery |
Keywords | Field | DocType |
Patient modeling,Anatomical landmark,Statistical shape model,Interventional,X-ray,Imaging | Computer vision,Embedding,Medical imaging,Navigation assistance,Computed tomography,Artificial intelligence,Scanner,Landmark,Workflow,Medicine,Angiography | Journal |
Volume | Issue | ISSN |
14 | 1 | 1861-6429 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xia Zhong | 1 | 0 | 3.04 |
| Norbert Strobel | 2 | 136 | 23.42 |
| Annette Birkhold | 3 | 0 | 4.39 |
| Markus Kowarschik | 4 | 222 | 42.67 |
| Rebecca Fahrig | 5 | 104 | 31.90 |
| Andreas K. Maier | 6 | 560 | 178.76 |