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Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework
The Adolescent Brain Cognitive Development (ABCD) Neurocognitive Prediction Challenge (ABCD-NP-Challenge) is a community-driven competition that challenges competitors to develop algorithms to predict fluid intelligence scores from T1-w MRI images. In this work, a two-step deep learning pipeline is...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345352/ https://www.ncbi.nlm.nih.gov/pubmed/35917308 http://dx.doi.org/10.1371/journal.pone.0268707 |
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author | Li, Mingliang Jiang, Mingfeng Zhang, Guangming Liu, Yujun Zhou, Xiaobo |
author_facet | Li, Mingliang Jiang, Mingfeng Zhang, Guangming Liu, Yujun Zhou, Xiaobo |
author_sort | Li, Mingliang |
collection | PubMed |
description | The Adolescent Brain Cognitive Development (ABCD) Neurocognitive Prediction Challenge (ABCD-NP-Challenge) is a community-driven competition that challenges competitors to develop algorithms to predict fluid intelligence scores from T1-w MRI images. In this work, a two-step deep learning pipeline is proposed to improve the prediction accuracy of fluid intelligence scores. In terms of the first step, the main contributions of this study include the following: (1) the concepts of the residual network (ResNet) and the squeeze-and-excitation network (SENet) are utilized to improve the original 3D U-Net; (2) in the segmentation process, the pixels in symmetrical brain regions are assigned the same label; (3) to remove redundant background information from the segmented regions of interest (ROIs), a minimum bounding cube (MBC) is used to enclose the ROIs. This new segmentation structure can greatly improve the segmentation performance of the ROIs in the brain as compared with the classical convolutional neural network (CNN), which yields a Dice coefficient of 0.8920. In the second stage, MBCs are used to train neural network regression models for enhanced nonlinearity. The fluid intelligence score prediction results of the proposed method are found to be superior to those of current state-of-the-art approaches, and the proposed method achieves a mean square error (MSE) of 82.56 on a test data set, which reflects a very competitive performance. |
format | Online Article Text |
id | pubmed-9345352 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-93453522022-08-03 Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework Li, Mingliang Jiang, Mingfeng Zhang, Guangming Liu, Yujun Zhou, Xiaobo PLoS One Research Article The Adolescent Brain Cognitive Development (ABCD) Neurocognitive Prediction Challenge (ABCD-NP-Challenge) is a community-driven competition that challenges competitors to develop algorithms to predict fluid intelligence scores from T1-w MRI images. In this work, a two-step deep learning pipeline is proposed to improve the prediction accuracy of fluid intelligence scores. In terms of the first step, the main contributions of this study include the following: (1) the concepts of the residual network (ResNet) and the squeeze-and-excitation network (SENet) are utilized to improve the original 3D U-Net; (2) in the segmentation process, the pixels in symmetrical brain regions are assigned the same label; (3) to remove redundant background information from the segmented regions of interest (ROIs), a minimum bounding cube (MBC) is used to enclose the ROIs. This new segmentation structure can greatly improve the segmentation performance of the ROIs in the brain as compared with the classical convolutional neural network (CNN), which yields a Dice coefficient of 0.8920. In the second stage, MBCs are used to train neural network regression models for enhanced nonlinearity. The fluid intelligence score prediction results of the proposed method are found to be superior to those of current state-of-the-art approaches, and the proposed method achieves a mean square error (MSE) of 82.56 on a test data set, which reflects a very competitive performance. Public Library of Science 2022-08-02 /pmc/articles/PMC9345352/ /pubmed/35917308 http://dx.doi.org/10.1371/journal.pone.0268707 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Li, Mingliang Jiang, Mingfeng Zhang, Guangming Liu, Yujun Zhou, Xiaobo Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework |
title | Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework |
title_full | Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework |
title_fullStr | Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework |
title_full_unstemmed | Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework |
title_short | Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework |
title_sort | prediction of fluid intelligence from t1-w mri images: a precise two-step deep learning framework |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345352/ https://www.ncbi.nlm.nih.gov/pubmed/35917308 http://dx.doi.org/10.1371/journal.pone.0268707 |
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