Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Title: Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Volume: 3

Author(s): Elsa D. Angelini, Olivier Clatz, Emmanuel Mandonnet, Ender Konukoglu, Laurent Capelle and Hugues Duffau

Affiliation:

Keywords: 3D segmentation, tumor volume, coefficient of variation, MRI spectroscopy imaging, Glioma growth

Abstract: Tracking gliomas dynamics on MRI has became more and more important for therapeutic management. Powerful computational tools have been recently developed in this context enabling in silico growth on a virtual brain that can be matched with real 3D segmented evolution through registration between atlases and patient brain MRI data. In this paper, we provide an extensive review of existing algorithms for the three computational tasks involved in patient-specific tumor modeling: image segmentation, image registration, and in silico growth modelling (with special emphasis on the proliferation-diffusion model). Accuracy and limits of the reviewed algorithms are systematically discussed. Finally applications of these methods for both clinical practice and fundamental research are also discussed.

Cite this article as:

Angelini D. Elsa, Clatz Olivier, Mandonnet Emmanuel, Konukoglu Ender, Capelle Laurent and Duffau Hugues, Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications, Current Medical Imaging 2007; 3 (4) . https://dx.doi.org/10.2174/157340507782446241

DOI https://dx.doi.org/10.2174/157340507782446241	Print ISSN 1573-4056
Publisher Name Bentham Science Publisher	Online ISSN 1875-6603