BundleMAP : Anatomically localized classification, regression, and hypothesis testing in diffusion MRI
(2017) In Pattern Recognition 63. p.593-600- Abstract
Diffusion MRI (dMRI) provides rich information on the white matter of the human brain, enabling insight into neurological disease, normal aging, and neuroplasticity. We present BundleMAP, an approach to extracting features from dMRI data that can be used for supervised classification, regression, and hypothesis testing. Our features are based on aggregating measurements along nerve fiber bundles, enabling visualization and anatomical interpretation. The main idea behind BundleMAP is to use the ISOMAP manifold learning technique to jointly parametrize nerve fiber bundles. We combine this idea with mechanisms for outlier removal and feature selection to obtain a practical machine learning pipeline. We demonstrate that it increases... (More)
Diffusion MRI (dMRI) provides rich information on the white matter of the human brain, enabling insight into neurological disease, normal aging, and neuroplasticity. We present BundleMAP, an approach to extracting features from dMRI data that can be used for supervised classification, regression, and hypothesis testing. Our features are based on aggregating measurements along nerve fiber bundles, enabling visualization and anatomical interpretation. The main idea behind BundleMAP is to use the ISOMAP manifold learning technique to jointly parametrize nerve fiber bundles. We combine this idea with mechanisms for outlier removal and feature selection to obtain a practical machine learning pipeline. We demonstrate that it increases accuracy of disease detection and estimation of disease activity, and that it improves the power of statistical tests.
(Less)
- author
- Khatami, Mohammad
; Schmidt-Wilcke, Tobias
; Sundgren, Pia C.
LU
; Abbasloo, Amin ; Schölkopf, Bernhard and Schultz, Thomas
- organization
- publishing date
- 2017-03-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Classification, Diffusion MRI, Disease detection, Fiber tracking, Manifold learning, Regression, Support vector machines
- in
- Pattern Recognition
- volume
- 63
- pages
- 8 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:84998996730
- wos:000389785900050
- ISSN
- 0031-3203
- DOI
- 10.1016/j.patcog.2016.09.020
- language
- English
- LU publication?
- yes
- id
- 6325aa4b-2b90-4a1f-9fc9-9acdf3b683ab
- date added to LUP
- 2017-02-03 09:43:12
- date last changed
- 2025-01-07 05:53:22
@article{6325aa4b-2b90-4a1f-9fc9-9acdf3b683ab, abstract = {{<p>Diffusion MRI (dMRI) provides rich information on the white matter of the human brain, enabling insight into neurological disease, normal aging, and neuroplasticity. We present BundleMAP, an approach to extracting features from dMRI data that can be used for supervised classification, regression, and hypothesis testing. Our features are based on aggregating measurements along nerve fiber bundles, enabling visualization and anatomical interpretation. The main idea behind BundleMAP is to use the ISOMAP manifold learning technique to jointly parametrize nerve fiber bundles. We combine this idea with mechanisms for outlier removal and feature selection to obtain a practical machine learning pipeline. We demonstrate that it increases accuracy of disease detection and estimation of disease activity, and that it improves the power of statistical tests.</p>}}, author = {{Khatami, Mohammad and Schmidt-Wilcke, Tobias and Sundgren, Pia C. and Abbasloo, Amin and Schölkopf, Bernhard and Schultz, Thomas}}, issn = {{0031-3203}}, keywords = {{Classification; Diffusion MRI; Disease detection; Fiber tracking; Manifold learning; Regression; Support vector machines}}, language = {{eng}}, month = {{03}}, pages = {{593--600}}, publisher = {{Elsevier}}, series = {{Pattern Recognition}}, title = {{BundleMAP : Anatomically localized classification, regression, and hypothesis testing in diffusion MRI}}, url = {{http://dx.doi.org/10.1016/j.patcog.2016.09.020}}, doi = {{10.1016/j.patcog.2016.09.020}}, volume = {{63}}, year = {{2017}}, }