Soft Tissue Tracking for Minimally Invasive Surgery: Learning Local Deformation Online View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2008

AUTHORS

Peter Mountney , Guang-Zhong Yang

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. More... »

PAGES

364-372

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-85990-1_44

DOI

http://dx.doi.org/10.1007/978-3-540-85990-1_44

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1047532022

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18982626


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