Name
Abstract | ||
|---|---|---|
Soft tissue deformation induced by craniotomy and tissue manipulation (brain shift) limits the use of preoperative image overlay in an image-guided neurosurgery, and therefore reduces the accuracy of the surgery as a consequence. An inexpensive modality to compensate for the brain shift in real-time is Ultrasound (US). The core subject of research in this context is the non-rigid registration of preoperative MR and intraoperative US images. In this work, we propose a learning based approach to address this challenge. Resolving intraoperative brain shift is considered as an imitation game, where the optimal action (displacement) for each landmark on MR is trained with a multi-task network. The result shows a mean target error of 1.21 +/- 0.55 mm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01045-4_15 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Conference | 11042 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xia Zhong | 1 | 0 | 3.04 |
| Siming Bayer | 2 | 6 | 1.90 |
| Nishant Ravikumar | 3 | 0 | 0.68 |
| Norbert Strobel | 4 | 136 | 23.42 |
| Annette Birkhold | 5 | 0 | 4.39 |
| Markus Kowarschik | 6 | 222 | 42.67 |
| Rebecca Fahrig | 7 | 104 | 31.90 |
| Andreas K. Maier | 8 | 560 | 178.76 |