Tissue Segmentation Using Sparse Non-negative Matrix Factorization ofÂ Spherical Mean Diffusion MRI Data

Peng Sun; Ye Wu; Geng Chen; Jun Wu; Dinggang Shen; Pew Thian Yap

doi:10.1007/978-3-030-05831-9_6

Tissue Segmentation Using Sparse Non-negative Matrix Factorization ofÂ Spherical Mean Diffusion MRI Data

Peng Sun, Ye Wu, Geng Chen, Jun Wu, Dinggang Shen, Pew Thian Yap

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Mathematics and Visualization
Editors	Chantal M.W. Tax, Lipeng Ning, Elisenda Bonet-Carne, Francesco Grussu, Farshid Sepehrband
Publisher	Springer Heidelberg
Pages	69-76
Number of pages	8
Edition	226249
ISBN (Print)	9783030058302
DOIs	https://doi.org/10.1007/978-3-030-05831-9_6
Publication status	Published - 2019
Externally published	Yes
Event	International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 2018 Sep 20 → 2018 Sep 20

Publication series

Name	Mathematics and Visualization
Number	226249
ISSN (Print)	1612-3786
ISSN (Electronic)	2197-666X

Conference

Conference	International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	18/9/20 → 18/9/20

Keywords

Diffusion MRI
Sparse NMF
Spherical mean
Tissue segmentation

ASJC Scopus subject areas

Modelling and Simulation
Geometry and Topology
Computer Graphics and Computer-Aided Design
Applied Mathematics

Access to Document

10.1007/978-3-030-05831-9_6

Cite this

Sun, P., Wu, Y., Chen, G., Wu, J., Shen, D., & Yap, P. T. (2019). Tissue Segmentation Using Sparse Non-negative Matrix Factorization ofÂ Spherical Mean Diffusion MRI Data. In C. M. W. Tax, L. Ning, E. Bonet-Carne, F. Grussu, & F. Sepehrband (Eds.), Mathematics and Visualization (226249 ed., pp. 69-76). (Mathematics and Visualization; No. 226249). Springer Heidelberg. https://doi.org/10.1007/978-3-030-05831-9_6

Tissue Segmentation Using Sparse Non-negative Matrix Factorization ofÂ Spherical Mean Diffusion MRI Data. / Sun, Peng; Wu, Ye; Chen, Geng et al.

Mathematics and Visualization. ed. / Chantal M.W. Tax; Lipeng Ning; Elisenda Bonet-Carne; Francesco Grussu; Farshid Sepehrband. 226249. ed. Springer Heidelberg, 2019. p. 69-76 (Mathematics and Visualization; No. 226249).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sun, P, Wu, Y, Chen, G, Wu, J, Shen, D & Yap, PT 2019, Tissue Segmentation Using Sparse Non-negative Matrix Factorization ofÂ Spherical Mean Diffusion MRI Data. in CMW Tax, L Ning, E Bonet-Carne, F Grussu & F Sepehrband (eds), Mathematics and Visualization. 226249 edn, Mathematics and Visualization, no. 226249, Springer Heidelberg, pp. 69-76, International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 18/9/20. https://doi.org/10.1007/978-3-030-05831-9_6

Sun P, Wu Y, Chen G, Wu J, Shen D, Yap PT. Tissue Segmentation Using Sparse Non-negative Matrix Factorization ofÂ Spherical Mean Diffusion MRI Data. In Tax CMW, Ning L, Bonet-Carne E, Grussu F, Sepehrband F, editors, Mathematics and Visualization. 226249 ed. Springer Heidelberg. 2019. p. 69-76. (Mathematics and Visualization; 226249). https://doi.org/10.1007/978-3-030-05831-9_6

Sun, Peng ; Wu, Ye ; Chen, Geng et al. / Tissue Segmentation Using Sparse Non-negative Matrix Factorization ofÂ Spherical Mean Diffusion MRI Data. Mathematics and Visualization. editor / Chantal M.W. Tax ; Lipeng Ning ; Elisenda Bonet-Carne ; Francesco Grussu ; Farshid Sepehrband. 226249. ed. Springer Heidelberg, 2019. pp. 69-76 (Mathematics and Visualization; 226249).

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abstract = "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.",

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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.

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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.

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Tissue Segmentation Using Sparse Non-negative Matrix Factorization ofÂ Spherical Mean Diffusion MRI Data

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Keywords

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