PublicationDetail

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

A. Guha Roy, S. Conjeti, S. Carlier, A. Konig, A. Kastrati, P. K. Dutta, A. Laine, N. Navab, D. Sheet, A. Katouzian
Bag of forests for modelling of tissue energy interaction in optical coherence tomography for atherosclerotic plaque susceptibility assessment
Proceedings of International Symposium on Biomedical Imaging (ISBI), Brooklyn, NY, USA, April 2015 (bib)

Atherosclerosis assessment using the high resolution of intravascular optical coherence tomography (IV-OCT) enables the visualization of tissue pools which are susceptible to rupture. However, the stochastic nature of OCT speckles leads to subjectivity in interpretation. In this paper, we present a framework for to OCT tissue energy interaction driven marker for identifying susceptible pool of tissues. The local statistics of OCT speckles is coupled with estimate of optical attenuation and signal confidence measures in a multiscale approach. Further, a bag of random forests is trained on a bank of 22 OCT cross-sections where susceptible tissues have been identified by correlating with the histology. Evaluating the performance on 22 other diverse cross-sections substantiates robustness of the framework. It is envisaged that this framework would help in objective assessment of plaque susceptibility and aid in more confident decision making during interventions.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.



Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif