Cargando…
Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging
Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, a...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723284/ https://www.ncbi.nlm.nih.gov/pubmed/33290403 http://dx.doi.org/10.1371/journal.pone.0242712 |
_version_ | 1783620313119981568 |
---|---|
author | Ahmed, Samsuddin Kim, Byeong C. Lee, Kun Ho Jung, Ho Yub |
author_facet | Ahmed, Samsuddin Kim, Byeong C. Lee, Kun Ho Jung, Ho Yub |
author_sort | Ahmed, Samsuddin |
collection | PubMed |
description | Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design. |
format | Online Article Text |
id | pubmed-7723284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77232842020-12-16 Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging Ahmed, Samsuddin Kim, Byeong C. Lee, Kun Ho Jung, Ho Yub PLoS One Research Article Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design. Public Library of Science 2020-12-08 /pmc/articles/PMC7723284/ /pubmed/33290403 http://dx.doi.org/10.1371/journal.pone.0242712 Text en © 2020 Ahmed et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ahmed, Samsuddin Kim, Byeong C. Lee, Kun Ho Jung, Ho Yub Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging |
title | Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging |
title_full | Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging |
title_fullStr | Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging |
title_full_unstemmed | Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging |
title_short | Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging |
title_sort | ensemble of roi-based convolutional neural network classifiers for staging the alzheimer disease spectrum from magnetic resonance imaging |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723284/ https://www.ncbi.nlm.nih.gov/pubmed/33290403 http://dx.doi.org/10.1371/journal.pone.0242712 |
work_keys_str_mv | AT ahmedsamsuddin ensembleofroibasedconvolutionalneuralnetworkclassifiersforstagingthealzheimerdiseasespectrumfrommagneticresonanceimaging AT kimbyeongc ensembleofroibasedconvolutionalneuralnetworkclassifiersforstagingthealzheimerdiseasespectrumfrommagneticresonanceimaging AT leekunho ensembleofroibasedconvolutionalneuralnetworkclassifiersforstagingthealzheimerdiseasespectrumfrommagneticresonanceimaging AT junghoyub ensembleofroibasedconvolutionalneuralnetworkclassifiersforstagingthealzheimerdiseasespectrumfrommagneticresonanceimaging AT ensembleofroibasedconvolutionalneuralnetworkclassifiersforstagingthealzheimerdiseasespectrumfrommagneticresonanceimaging |