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Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm
The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algo...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4828512/ https://www.ncbi.nlm.nih.gov/pubmed/27110218 http://dx.doi.org/10.1007/s10044-015-0492-0 |
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author | Maglietta, Rosalia Amoroso, Nicola Boccardi, Marina Bruno, Stefania Chincarini, Andrea Frisoni, Giovanni B. Inglese, Paolo Redolfi, Alberto Tangaro, Sabina Tateo, Andrea Bellotti, Roberto |
author_facet | Maglietta, Rosalia Amoroso, Nicola Boccardi, Marina Bruno, Stefania Chincarini, Andrea Frisoni, Giovanni B. Inglese, Paolo Redolfi, Alberto Tangaro, Sabina Tateo, Andrea Bellotti, Roberto |
author_sort | Maglietta, Rosalia |
collection | PubMed |
description | The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice’s index of [Formula: see text] ([Formula: see text] ) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi. |
format | Online Article Text |
id | pubmed-4828512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-48285122016-04-21 Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm Maglietta, Rosalia Amoroso, Nicola Boccardi, Marina Bruno, Stefania Chincarini, Andrea Frisoni, Giovanni B. Inglese, Paolo Redolfi, Alberto Tangaro, Sabina Tateo, Andrea Bellotti, Roberto Pattern Anal Appl Industrial and Commercial Application The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice’s index of [Formula: see text] ([Formula: see text] ) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi. Springer London 2015-07-09 2016 /pmc/articles/PMC4828512/ /pubmed/27110218 http://dx.doi.org/10.1007/s10044-015-0492-0 Text en © The Author(s) 2015 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 | Industrial and Commercial Application Maglietta, Rosalia Amoroso, Nicola Boccardi, Marina Bruno, Stefania Chincarini, Andrea Frisoni, Giovanni B. Inglese, Paolo Redolfi, Alberto Tangaro, Sabina Tateo, Andrea Bellotti, Roberto Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm |
title | Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm |
title_full | Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm |
title_fullStr | Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm |
title_full_unstemmed | Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm |
title_short | Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm |
title_sort | automated hippocampal segmentation in 3d mri using random undersampling with boosting algorithm |
topic | Industrial and Commercial Application |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4828512/ https://www.ncbi.nlm.nih.gov/pubmed/27110218 http://dx.doi.org/10.1007/s10044-015-0492-0 |
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