Graph Based Spatial Position Mapping of Low-grade Gliomas

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.

[1]  R Grant,et al.  Guidelines on management of low‐grade gliomas: report of an EFNS–EANO * Task Force , 2010, European journal of neurology.

[2]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[4]  Zoran Obradovic,et al.  Subjective Probability , 1994 .

[5]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[6]  Nassir Navab,et al.  Optical flow estimation with uncertainties through dynamic MRFs , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[8]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[9]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[10]  J. Dunn Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[11]  Vladimir Kolmogorov,et al.  Feature Correspondence Via Graph Matching: Models and Global Optimization , 2008, ECCV.

[12]  Nassir Navab,et al.  Dense image registration through MRFs and efficient linear programming , 2008, Medical Image Anal..

[13]  Anssi Auvinen,et al.  Incidence of gliomas by anatomic location. , 2007, Neuro-oncology.

[14]  Christopher J. Taylor,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009 , 2009, Lecture Notes in Computer Science.

[15]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[16]  Hugues Duffau,et al.  Surgery of low-grade gliomas: towards a ‘functional neurooncology’ , 2009, Current opinion in oncology.

[17]  Ben Glocker,et al.  Graphical Models and Deformable Diffeomorphic Population Registration Using Global and Local Metrics , 2009, MICCAI.

[18]  Jagath C. Rajapakse,et al.  Learning functional structure from fMR images , 2006, NeuroImage.

[19]  Laurent Capelle,et al.  Preferential brain locations of low‐grade gliomas , 2004, Cancer.

[20]  Nikos Komodakis,et al.  Clustering via LP-based Stabilities , 2008, NIPS.