Chapter title |
Multi-modal Registration Improves Group Discrimination in Pediatric Traumatic Brain Injury
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Chapter number | 4 |
Book title |
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
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Published in |
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries : second International Workshop, BrainLes 2016, with the challenges on BRATS, ISLES and mTOP 2016, held in conjunction with MICCAI 2016, Athens, Greece, Octob..., October 2016
|
DOI | 10.1007/978-3-319-55524-9_4 |
Pubmed ID | |
Book ISBNs |
978-3-31-955523-2, 978-3-31-955524-9
|
Authors |
Emily L. Dennis, Faisal Rashid, Julio Villalon-Reina, Gautam Prasad, Joshua Faskowitz, Talin Babikian, Richard Mink, Christopher Babbitt, Jeffrey Johnson, Christopher C. Giza, Robert F. Asarnow, Paul M. Thompson |
Abstract |
Traumatic brain injury (TBI) can disrupt the white matter (WM) integrity in the brain, leading to functional and cognitive disruptions that may persist for years. There is considerable heterogeneity within the patient group, which complicates group analyses. Here we present improvements to a tract identification workflow, automated multi-atlas tract extraction (autoMATE), evaluating the effects of improved registration. Use of study-specific template improved group classification accuracy over the standard workflow. The addition of a multi-modal registration that includes information from diffusion weighted imaging (DWI), T1-weighted, and Fluid-Attenuated Inversion Recovery (FLAIR) further improved classification accuracy. We also examined whether particular tracts contribute more to group classification than others. Parts of the corpus callosum contributed most, and there were unexpected asymmetries between bilateral tracts. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 8 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Professor | 2 | 25% |
Student > Master | 2 | 25% |
Professor > Associate Professor | 1 | 13% |
Unknown | 3 | 38% |
Readers by discipline | Count | As % |
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Computer Science | 2 | 25% |
Psychology | 2 | 25% |
Neuroscience | 1 | 13% |
Medicine and Dentistry | 1 | 13% |
Unknown | 2 | 25% |