Seghier, ML;
Kolanko, MA;
Leff, AP;
Jager, HR;
Gregoire, SM;
Werring, DJ;
(2011)
Microbleed Detection Using Automated Segmentation (MIDAS): A New Method Applicable to Standard Clinical MR Images.
PLOS ONE
, 6
(3)
, Article e17547. 10.1371/journal.pone.0017547.
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Abstract
Background: Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction.Methodology/Principal Findings: Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an "extra" tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (>= 2) lobar microbleeds.Conclusions/Significance: MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds.
| Type: | Article |
|---|---|
| Title: | Microbleed Detection Using Automated Segmentation (MIDAS): A New Method Applicable to Standard Clinical MR Images |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pone.0017547 |
| Publisher version: | http://dx.doi.org/10.1371/journal.pone.0017547 |
| Language: | English |
| Additional information: | © 2011 Seghier et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Mohamed Seghier and Alex Leff were funded by the Wellcome Trust. Simone Gregoire was supported by a grant from The Stroke Association. David Werring is supported by a Department of Health and Higher Educational and Funding Council for England Clinical Senior Lectureship Award. This work was undertaken at UCLH/UCL, which received a proportion of funding from the UK Department of Health's National Institute for Health Research Biomedical Research Centers funding scheme (UCLH/UCL Comprehensive Biomedical Research Trust). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
| Keywords: | CEREBRAL AMYLOID ANGIOPATHY, BRAIN MICROBLEEDS, UNIFIED SEGMENTATION, PREVALENCE, AGREEMENT, RISK |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1301876 |
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