Chapter title |
Soft tissue tracking for minimally invasive surgery: learning local deformation online.
|
---|---|
Chapter number | 44 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2008
|
DOI | 10.1007/978-3-540-85990-1_44 |
Pubmed ID | |
Book ISBNs |
978-3-54-085989-5, 978-3-54-085990-1
|
Authors |
Peter Mountney, Guang-Zhong Yang, Mountney, Peter, Yang, Guang-Zhong |
Abstract |
Accurate estimation and tracking of dynamic tissue deformation is important to motion compensation, intra-operative surgical guidance and navigation in minimally invasive surgery. Current approaches to tissue deformation tracking are generally based on machine vision techniques for natural scenes which are not well suited to MIS because tissue deformation cannot be easily modeled by using ad hoc representations. Such techniques do not deal well with inter-reflection changes and may be susceptible to instrument occlusion. The purpose of this paper is to present an online learning based feature tracking method suitable for in vivo applications. It makes no assumptions about the type of image transformations and visual characteristics, and is updated continuously as the tracking progresses. The performance of the algorithm is compared with existing tracking algorithms and validated on simulated, as well as in vivo cardiovascular and abdominal MIS data. The strength of the algorithm in dealing with drift and occlusion is validated and the practical value of the method is demonstrated by decoupling cardiac and respiratory motion in robotic assisted surgery. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 2 | 4% |
Unknown | 46 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 12 | 25% |
Student > Ph. D. Student | 10 | 21% |
Student > Master | 8 | 17% |
Student > Bachelor | 5 | 10% |
Student > Doctoral Student | 4 | 8% |
Other | 5 | 10% |
Unknown | 4 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 24 | 50% |
Computer Science | 11 | 23% |
Medicine and Dentistry | 4 | 8% |
Neuroscience | 1 | 2% |
Agricultural and Biological Sciences | 1 | 2% |
Other | 0 | 0% |
Unknown | 7 | 15% |