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Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm
Brain volumes computed from magnetic resonance images have potential for assisting with the diagnosis of individual dementia patients, provided that they have low measurement error and high reliability. In this paper we describe and validate icobrain dm, an automatic tool that segments brain structu...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082216/ https://www.ncbi.nlm.nih.gov/pubmed/32193172 http://dx.doi.org/10.1016/j.nicl.2020.102243 |
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author | Struyfs, Hanne Sima, Diana Maria Wittens, Melissa Ribbens, Annemie Pedrosa de Barros, Nuno Phan, Thanh Vân Ferraz Meyer, Maria Ines Claes, Lene Niemantsverdriet, Ellis Engelborghs, Sebastiaan Van Hecke, Wim Smeets, Dirk |
author_facet | Struyfs, Hanne Sima, Diana Maria Wittens, Melissa Ribbens, Annemie Pedrosa de Barros, Nuno Phan, Thanh Vân Ferraz Meyer, Maria Ines Claes, Lene Niemantsverdriet, Ellis Engelborghs, Sebastiaan Van Hecke, Wim Smeets, Dirk |
author_sort | Struyfs, Hanne |
collection | PubMed |
description | Brain volumes computed from magnetic resonance images have potential for assisting with the diagnosis of individual dementia patients, provided that they have low measurement error and high reliability. In this paper we describe and validate icobrain dm, an automatic tool that segments brain structures that are relevant for differential diagnosis of dementia, such as the hippocampi and cerebral lobes. Experiments were conducted in comparison to the widely used FreeSurfer software. The hippocampus segmentations were compared against manual segmentations, with significantly higher Dice coefficients obtained with icobrain dm (25–75th quantiles: 0.86–0.88) than with FreeSurfer (25–75th quantiles: 0.80–0.83). Other brain structures were also compared against manual delineations, with icobrain dm showing lower volumetric errors overall. Test-retest experiments show that the precision of all measurements is higher for icobrain dm than for FreeSurfer except for the parietal cortex volume. Finally, when comparing volumes obtained from Alzheimer's disease patients against age-matched healthy controls, all measures achieved high diagnostic performance levels when discriminating patients from cognitively healthy controls, with the temporal cortex volume measured by icobrain dm reaching the highest diagnostic performance level (area under the receiver operating characteristic curve = 0.99) in this dataset. |
format | Online Article Text |
id | pubmed-7082216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-70822162020-03-24 Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm Struyfs, Hanne Sima, Diana Maria Wittens, Melissa Ribbens, Annemie Pedrosa de Barros, Nuno Phan, Thanh Vân Ferraz Meyer, Maria Ines Claes, Lene Niemantsverdriet, Ellis Engelborghs, Sebastiaan Van Hecke, Wim Smeets, Dirk Neuroimage Clin Regular Article Brain volumes computed from magnetic resonance images have potential for assisting with the diagnosis of individual dementia patients, provided that they have low measurement error and high reliability. In this paper we describe and validate icobrain dm, an automatic tool that segments brain structures that are relevant for differential diagnosis of dementia, such as the hippocampi and cerebral lobes. Experiments were conducted in comparison to the widely used FreeSurfer software. The hippocampus segmentations were compared against manual segmentations, with significantly higher Dice coefficients obtained with icobrain dm (25–75th quantiles: 0.86–0.88) than with FreeSurfer (25–75th quantiles: 0.80–0.83). Other brain structures were also compared against manual delineations, with icobrain dm showing lower volumetric errors overall. Test-retest experiments show that the precision of all measurements is higher for icobrain dm than for FreeSurfer except for the parietal cortex volume. Finally, when comparing volumes obtained from Alzheimer's disease patients against age-matched healthy controls, all measures achieved high diagnostic performance levels when discriminating patients from cognitively healthy controls, with the temporal cortex volume measured by icobrain dm reaching the highest diagnostic performance level (area under the receiver operating characteristic curve = 0.99) in this dataset. Elsevier 2020-03-12 /pmc/articles/PMC7082216/ /pubmed/32193172 http://dx.doi.org/10.1016/j.nicl.2020.102243 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article Struyfs, Hanne Sima, Diana Maria Wittens, Melissa Ribbens, Annemie Pedrosa de Barros, Nuno Phan, Thanh Vân Ferraz Meyer, Maria Ines Claes, Lene Niemantsverdriet, Ellis Engelborghs, Sebastiaan Van Hecke, Wim Smeets, Dirk Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm |
title | Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm |
title_full | Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm |
title_fullStr | Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm |
title_full_unstemmed | Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm |
title_short | Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm |
title_sort | automated mri volumetry as a diagnostic tool for alzheimer's disease: validation of icobrain dm |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082216/ https://www.ncbi.nlm.nih.gov/pubmed/32193172 http://dx.doi.org/10.1016/j.nicl.2020.102243 |
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