Evidence map›Paper›PMID 25127120›Full record

ArticlePloS one2014

Automated segmentation and quantification of white matter hyperintensities in acute ischemic stroke patients with cerebral infarction.

Jang-Zern Tsai, Syu-Jyun Peng, Yu-Wei Chen, Kuo-Wei Wang, Chen-Hua Li, Jing-Yi Wang, Chi-Jen Chen, Huey-Juan Lin, Eric Edward Smith, Hsiao-Kuang Wu and 3 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
0.6field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 52 citations in OpenAlex.

  1. Article
  2. Journal of Alzheimer's disease : JAD · 2026
    Article
  3. APOE4 modulates the association between DTI-ALPS index and Alzheimer's pathologies.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
  4. Article
  5. Daily fluid intake and brain amyloid deposition: A cohort study.Journal of Alzheimer's disease : JAD · 2025
    Article
  6. Segmenting Small Stroke Lesions with Novel Labeling Strategies.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025
    Article
  7. Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025
    Review
  8. Visual and Auditory Sensory Impairments Differentially Relate with Alzheimer's Pathology.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Segmentation of white matter hyperintensities onEuropean journal of nuclear medicine and molecular imaging · 2021
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 8 institutions in 2 countries.

Jang-Zern TsaiDepartment of Electrical Engineering, National Central University, Jhongli City, Taoyuan County, Taiwan.
Syu-Jyun PengDepartment of Electrical Engineering, National Central University, Jhongli City, Taoyuan County, Taiwan.
Yu-Wei ChenDepartment of Computer Science and Information Engineering, National Central University, Jhongli City, Taoyuan County, Taiwan; Department of Neurology, Landseed Hospital, Pingzhen City, Taoyuan County, Taiwan; Department of Neurology, National Taiwan University Hospital, Taipei City, Taiwan.
Kuo-Wei WangDepartment of Electrical Engineering, National Central University, Jhongli City, Taoyuan County, Taiwan; Department of Medical Imaging, Landseed Hospital, Pingzhen City, Taoyuan County, Taiwan.
Chen-Hua LiDepartment of Neurology, Landseed Hospital, Pingzhen City, Taoyuan County, Taiwan; Department of Neurology, National Taiwan University Hospital, Taipei City, Taiwan.
Jing-Yi WangDepartment of Neurology, Landseed Hospital, Pingzhen City, Taoyuan County, Taiwan.
Chi-Jen ChenDepartment of Radiology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.
Huey-Juan LinDepartment of Neurology, Chi-Mei Medical Center, Tainan City, Taiwan.
Eric Edward SmithDepartment of Clinical Neurosciences, Radiology and Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
Hsiao-Kuang WuDepartment of Computer Science and Information Engineering, National Central University, Jhongli City, Taoyuan County, Taiwan.
Sheng-Feng SungDivision of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan.
Poh-Shiow YehDepartment of Neurology, Chi-Mei Medical Center, Tainan City, Taiwan.
Yue-Loong HsinDepartment of Neurology, Chung Shan Medical University and Chung Shan Medical University Hospital, Taichung City, Taiwan; Biomedical Electronics Translational Research Center, National Chiao Tung University, Hsinchu City, Taiwan.
National Central University · TWChi Mei Medical Center · TWNational Taiwan University Hospital · TWChia-Yi Christian Hospital · TWLandseed Hospital · TWNational Yang Ming Chiao Tung University · TWTaipei Medical University-Shuang Ho Hospital · TWUniversity of Calgary · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

White matter hyperintensities (WMHs) of presumed vascular origin are common in ageing population, especially in patients with acute cerebral infarction and the volume has been reported to be associated with mental impairment and the risk of hemorrhage from antithrombotic agents. WMHs delineation can be computerized to minimize human bias. However, the presence of cerebral infarcts greatly degrades the accuracy of WMHs detection and thus limits the application of computerized delineation to patients with acute cerebral infarction. We propose a computer-assisted segmentation method to depict WMHs in the presence of cerebral infarcts in combined T1-weighted, fluid attenuation inversion recovery, and diffusion-weighted magnetic resonance imaging (MRI). The proposed method detects WMHs by empirical threshold and atlas information, with subtraction of white matter voxels affected by acute infarction. The method was derived using MRI from 25 hemispheres with WMHs only and 13 hemispheres with both WMHs and cerebral infarcts. Similarity index (SI) and correlation were utilized to assess the agreement between the new automated method and a gold standard visually guided semi-automated method done by an expert rater. The proposed WMHs segmentation approach produced average SI, sensitivity and specificity of 83.142±11.742, 84.154±16.086 and 99.988±0.029% with WMHs only and of 68.826±14.036, 74.381±18.473 and 99.956±0.054% with both WMHs and cerebral infarcts in the derivation cohort. The performance of the proposed method with an external validation cohort was also highly consistent with that of the experienced rater.

Indexed as

Magnetic Resonance ImagingAgedAged, 80 and overAlgorithmsCerebral InfarctionFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedReproducibility of ResultsWhite MatterWorkflow

Identifiers

PMID25127120
PMCPMC4134193
OpenAlexW2150673341

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.