TY - GEN
T1 - A large-scale manifold learning approach for brain tumor progression prediction
AU - Tran, Loc
AU - Banerjee, Deb
AU - Sun, Xiaoyan
AU - Wang, Jihong
AU - Kumar, Ashok J.
AU - Vinning, David
AU - McKenzie, Frederic D.
AU - Li, Yaohang
AU - Li, Jiang
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80054003638&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80054003638&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24319-6_33
DO - 10.1007/978-3-642-24319-6_33
M3 - Conference contribution
AN - SCOPUS:80054003638
SN - 9783642243189
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 265
EP - 272
BT - Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
T2 - 2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 18 September 2011
ER -