TY - GEN
T1 - Tissue Segmentation Using Sparse Non-negative Matrix Factorization of Spherical Mean Diffusion MRI Data
AU - Sun, Peng
AU - Wu, Ye
AU - Chen, Geng
AU - Wu, Jun
AU - Shen, Dinggang
AU - Yap, Pew Thian
N1 - Funding Information:
This work was supported in part by NIH grants (NS093842, EB022880, EB006733, EB009634, AG041721, MH100217, and AA012388) and NSFC grants (11671022 and 61502392).
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - In this paper, we present a method based on sparse non-negative matrix factorization (NMF) for brain tissue segmentation using diffusion MRI (DMRI) data. Unlike existing NMF-based approaches, in our method NMF is applied to the spherical mean data, computed on a per-shell basis, instead of the original diffusion-weighted images. This is motivated by the fact that the spherical mean is independent of the fiber orientation distribution and is only dependent on tissue microstructure. Applying NMF to the spherical mean data will hence allow tissue signal separation based solely on the microstructural properties, unconfounded by factors such as fiber dispersion and crossing. We show results explaining why applying NMF directly on the diffusion-weighted images fails and why our method is able to yield the expected outcome, producing tissue segmentation with greater accuracy.
AB - In this paper, we present a method based on sparse non-negative matrix factorization (NMF) for brain tissue segmentation using diffusion MRI (DMRI) data. Unlike existing NMF-based approaches, in our method NMF is applied to the spherical mean data, computed on a per-shell basis, instead of the original diffusion-weighted images. This is motivated by the fact that the spherical mean is independent of the fiber orientation distribution and is only dependent on tissue microstructure. Applying NMF to the spherical mean data will hence allow tissue signal separation based solely on the microstructural properties, unconfounded by factors such as fiber dispersion and crossing. We show results explaining why applying NMF directly on the diffusion-weighted images fails and why our method is able to yield the expected outcome, producing tissue segmentation with greater accuracy.
KW - Diffusion MRI
KW - Sparse NMF
KW - Spherical mean
KW - Tissue segmentation
UR - http://www.scopus.com/inward/record.url?scp=85066880886&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066880886&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-05831-9_6
DO - 10.1007/978-3-030-05831-9_6
M3 - Conference contribution
AN - SCOPUS:85066880886
SN - 9783030058302
T3 - Mathematics and Visualization
SP - 69
EP - 76
BT - Mathematics and Visualization
A2 - Tax, Chantal M.W.
A2 - Ning, Lipeng
A2 - Bonet-Carne, Elisenda
A2 - Grussu, Francesco
A2 - Sepehrband, Farshid
PB - Springer Heidelberg
T2 - International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 20 September 2018 through 20 September 2018
ER -