Name
Abstract | ||
|---|---|---|
We present a feasibility study using cloud resources for computing the deformable registration or non-rigid registration (NRR) of brain MR images for Image Guided Neurosurgery (IGNS). We consider the use of cloud resources in two scenarios. First, we describe a workflow implementation to enable speculative computation of registration to improve confidence in the result and assist in retrospective evaluation of the method. We evaluate the use of computing and storage capabilities of the cloud to handle more than 6 TB of images. Second, we evaluate the feasibility of large scale running NRR on the cloud to provide timely execution of the most time-consuming components of the registration in short duration of a brain surgery. Our preliminary results indicate that the cloud provides practical and cost-effective means to support IGNS. In addition, cloud resources could be used to improve the accuracy of NRR up to 57%. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CHASE.2016.43 | 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) |
Keywords | Field | DocType |
Image Registration,Cloud,Big Data,Large Scale,Speculative Execution,Image-Guided Neurosurgery | Systems engineering,Computer science,Real-time computing,Artificial intelligence,Image-Guided Therapy,Workflow,Computation,Computer vision,Cloud resources,Speculative execution,Big data,Image registration,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-5090-0944-2 | 0 | 0.34 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shahram Mohrehkesh | 1 | 48 | 6.17 |
| Andriy Fedorov | 2 | 171 | 16.54 |
| Arun Brahmavar Vishwanatha | 3 | 0 | 0.34 |
| Fotis Drakopoulos | 4 | 10 | 1.97 |
| Ron Kikinis | 5 | 6723 | 1071.86 |
| Nikos Chrisochoides | 6 | 672 | 59.09 |