title: Graph based spatial position mapping of low-grade gliomas.
abstract: Low-grade gliomas (WHO grade II) are diffusively infiltrative brain tumors arising from glial cells. Spatial classification that is usually based on cerebral lobes lacks accuracy and is far from being able to provide some pattern or statistical interpretation of their appearance. In this paper, we propose a novel approach to understand and infer position of low-grade gliomas using a graphical model. The problem is formulated as a graph topology optimization problem. Graph nodes correspond to extracted tumors and graph connections to the spatial and content dependencies among them. The task of spatial position mapping is then expressed as an unsupervised clustering problem, where cluster centers correspond to centers with position appearance prior, and cluster samples to nodes with strong statistical dependencies on their position with respect to the cluster center. Promising results using leave-one-out cross-validation outperform conventional dimensionality reduction methods and seem to coincide with conclusions drawn in physiological studies regarding the expected tumor spatial distributions and interactions.
authors: Laboratoire MAS, Ecole Centrale Paris, Chatenay Malabry, France.
authors: Sarah
authors: S
authors: Parisot
authors: Hugues
authors: H
authors: Duffau
authors: Stéphane
authors: S
authors: Chemouny
authors: Nikos
authors: N
authors: Paragios
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D001932:Brain Neoplasms / Q000473:pathology*; D003196:Computer Graphics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005910:Glioma / Q000378:metabolism* / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D009369:Neoplasms / Q000473:pathology