RT Journal Article
SR Electronic
T1 FiberNET: An ensemble deep learning framework for clustering white matter fibers
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 141036
DO 10.1101/141036
A1 Vikash Gupta
A1 Sophia I. Thomopoulos
A1 Faisal M. Rashid
A1 Paul M. Thompson
YR 2017
UL http://biorxiv.org/content/early/2017/05/22/141036.abstract
AB White matter tracts are commonly analyzed in studies of micro-structural integrity and anatomical connectivity in the brain. Over the last decade, it has been an open problem as to how best to cluster white matter fibers, extracted from whole-brain tractography, into anatomically meaningful groups. Some existing techniques use region of interest (ROI) based clustering, atlas-based labeling, or unsupervised spectral clustering. ROI-based clustering is popular for analyzing anatomical connectivity among a set of ROIs, but it does not always partition the brain into recognizable fiber bundles. Here we propose an approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which are then exploited to cluster white matter fibers. To achieve such clustering, we first need to re-parameterize the fibers in an intrinsic space. The clustering is performed in induced parameterized coordinates. To our knowledge, this is one of the first approaches for fiber clustering using deep learning techniques. The results show strong accuracy - on a par with or better than other state-of-the-art methods.