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Reproducible segmentation of white matter hyperintensities using a new statistical definition
OBJECTIVES: We present a method based on a proposed statistical definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples. MATERIALS AND METHODS: T1-weighted, T2-weighted,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5440501/ https://www.ncbi.nlm.nih.gov/pubmed/27943055 http://dx.doi.org/10.1007/s10334-016-0599-3 |
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author | Damangir, Soheil Westman, Eric Simmons, Andrew Vrenken, Hugo Wahlund, Lars-Olof Spulber, Gabriela |
author_facet | Damangir, Soheil Westman, Eric Simmons, Andrew Vrenken, Hugo Wahlund, Lars-Olof Spulber, Gabriela |
author_sort | Damangir, Soheil |
collection | PubMed |
description | OBJECTIVES: We present a method based on a proposed statistical definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples. MATERIALS AND METHODS: T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer’s disease) were used. The segmentation was performed using a proposed definition for WMH based on the one-tailed Kolmogorov–Smirnov test. RESULTS: The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85–0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83–0.94) that exceeded intra-rater similarity (Dice 0.75–0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed definition has better accuracy and reproducibility in the test dataset used. CONCLUSION: Overall, the presented definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10334-016-0599-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5440501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-54405012017-06-08 Reproducible segmentation of white matter hyperintensities using a new statistical definition Damangir, Soheil Westman, Eric Simmons, Andrew Vrenken, Hugo Wahlund, Lars-Olof Spulber, Gabriela MAGMA Research Article OBJECTIVES: We present a method based on a proposed statistical definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples. MATERIALS AND METHODS: T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer’s disease) were used. The segmentation was performed using a proposed definition for WMH based on the one-tailed Kolmogorov–Smirnov test. RESULTS: The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85–0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83–0.94) that exceeded intra-rater similarity (Dice 0.75–0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed definition has better accuracy and reproducibility in the test dataset used. CONCLUSION: Overall, the presented definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10334-016-0599-3) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2016-12-09 2017 /pmc/articles/PMC5440501/ /pubmed/27943055 http://dx.doi.org/10.1007/s10334-016-0599-3 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Article Damangir, Soheil Westman, Eric Simmons, Andrew Vrenken, Hugo Wahlund, Lars-Olof Spulber, Gabriela Reproducible segmentation of white matter hyperintensities using a new statistical definition |
title | Reproducible segmentation of white matter hyperintensities using a new statistical definition |
title_full | Reproducible segmentation of white matter hyperintensities using a new statistical definition |
title_fullStr | Reproducible segmentation of white matter hyperintensities using a new statistical definition |
title_full_unstemmed | Reproducible segmentation of white matter hyperintensities using a new statistical definition |
title_short | Reproducible segmentation of white matter hyperintensities using a new statistical definition |
title_sort | reproducible segmentation of white matter hyperintensities using a new statistical definition |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5440501/ https://www.ncbi.nlm.nih.gov/pubmed/27943055 http://dx.doi.org/10.1007/s10334-016-0599-3 |
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