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A fully automated method for quantifying and localizing white matter hyperintensities on MR images
Wu M, Rosano C, Rosano C, Butters M, Whyte E, Nable M, Crooks R, Meltzer CC et al.. Psychiatry research, 2006-11-13
Topic: Risk, prevention and care in population studies
White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy-connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies (e.g. [Taylor, W.D., MacFall, J.R., Steffens, D.C., Payne, M.E., Provenzale, J.M., Krishnan, K.R., 2003. Localization of age-associated white matter hyperintensities in late-life depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 27 (3), 539-544.]), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large NeuroImage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders.
Key findings
- White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression.
- This dataset was chosen because LLD subjects are known to have significant WMH burden.
- The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings).
- A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods.
- Progress in Neuro-Psychopharmacology and Biological Psychiatry. 27 (3), 539-544.]), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method.
Why it matters
This study describes and validates an automated method for measuring and locating white matter hyperintensities on brain MRI, which are associated with conditions including Alzheimer's disease. It is a technical imaging tool and does not test any treatment, cure, or prevention for Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 41/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, so that was left out: it earned 39 of the 96 points we could judge it on, which is 41 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Psychiatry research) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2006 — older than 20 years, still counted |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 194 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.8 citations a year over 22 year(s) since 2006 |
| Impact for its field and year | 3 / 6 | Cited 4.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 6 other studies in this library |
| Still being cited | 1 / 4 | Cited 13 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Howard Jay Aizenstein: h-index 78 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Pittsburgh: institution h-index 1,470 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 17097277) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 39 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 39 of the 96 points we could judge it on is 41 out of 100. |
| Total | 41 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.pscychresns.2006.09.003
- PubMed: 17097277
- Original source →
- Free full text: PubMed Central →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Wu, M., Rosano, C., Butters, M., Whyte, E., Nable, M., Crooks, R., et al. (2006). A fully automated method for quantifying and localizing white matter hyperintensities on MR images. Psychiatry research. https://doi.org/10.1016/j.pscychresns.2006.09.003
Vancouver
Wu M, Rosano C, Butters M, Whyte E, Nable M, Crooks R, et al. A fully automated method for quantifying and localizing white matter hyperintensities on MR images. Psychiatry research. 2006. doi:10.1016/j.pscychresns.2006.09.003. PMID: 17097277.
BibTeX
@article{wu2006-17097277,
author = {Wu, M. and Rosano, C. and Butters, M. and Whyte, E. and Nable, M. and Crooks, R. and Meltzer, C. C. and others},
title = {{A fully automated method for quantifying and localizing white matter hyperintensities on MR images}},
journal = {Psychiatry research},
year = {2006},
doi = {10.1016/j.pscychresns.2006.09.003},
pmid = {17097277},
url = {https://doi.org/10.1016/j.pscychresns.2006.09.003}
}
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