A large-scale manifold learning approach for brain tumor progression prediction

Authors:
Loc Tran;Deb Banerjee;Xiaoyan Sun;Jihong Wang;Ashok J. Kumar;David Vinning;Frederic D. McKenzie;Yaohang Li;Jiang Li
Affiliations:
Departments of ECE, Old Dominion University, Norfolk, VA;Departments of ECE, Old Dominion University, Norfolk, VA;Departments of ECE, Old Dominion University, Norfolk, VA;Diagnostic Imaging, University of Texas, MD Anderson Cancer Center, Houston, TX;Diagnostic Imaging, University of Texas, MD Anderson Cancer Center, Houston, TX;Diagnostic Imaging, University of Texas, MD Anderson Cancer Center, Houston, TX;MSVE, Old Dominion University, Norfolk, VA;CS, Old Dominion University, Norfolk, VA;Departments of ECE, Old Dominion University, Norfolk, VA
Venue:
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Year:
2011

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Abstract

We present a novel manifold learning approach to efficiently identify low-dimensional structures, known as manifolds, embedded in large-scale, high dimensional MRI datasets for brain tumor growth prediction. The datasets consist of a series of MRI scans for three patients with tumor and progressed regions identified. We attempt to identify low dimensional manifolds for tumor, progressed and normal tissues, and most importantly, to verify if the progression manifold exists - the bridge between tumor and normal manifolds. By mapping the bridge manifold back to MRI image space, this method has the potential to predict tumor progression, thereby, greatly benefiting patient management. Preliminary results supported our hypothesis: normal and tumor manifolds are well separated in a low dimensional space and the progressed manifold is found to lie roughly between them but closer to the tumor manifold.