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A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure
Accurately extracting brain tissue is a critical and primary step in brain neuroimaging research. Due to the differences in brain size and structure between humans and nonhuman primates, the performance of the existing tools for brain tissue extraction, working on macaque brain MRI, is constrained....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870262/ https://www.ncbi.nlm.nih.gov/pubmed/35204023 http://dx.doi.org/10.3390/brainsci12020260 |
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author | Wang, Qianshan Fei, Hong Abdu Nasher, Saddam Naji Xia, Xiaoluan Li, Haifang |
author_facet | Wang, Qianshan Fei, Hong Abdu Nasher, Saddam Naji Xia, Xiaoluan Li, Haifang |
author_sort | Wang, Qianshan |
collection | PubMed |
description | Accurately extracting brain tissue is a critical and primary step in brain neuroimaging research. Due to the differences in brain size and structure between humans and nonhuman primates, the performance of the existing tools for brain tissue extraction, working on macaque brain MRI, is constrained. A new transfer learning training strategy was utilized to address the limitations, such as insufficient training data and unsatisfactory model generalization ability, when deep neural networks processing the limited samples of macaque magnetic resonance imaging(MRI). First, the project combines two human brain MRI data modes to pre-train the neural network, in order to achieve faster training and more accurate brain extraction. Then, a residual network structure in the U-Net model was added, in order to propose a ResTLU-Net model that aims to improve the generalization ability of multiple research sites data. The results demonstrated that the ResTLU-Net, combined with the proposed transfer learning strategy, achieved comparable accuracy for the macaque brain MRI extraction tasks on different macaque brain MRI volumes that were produced by various medical centers. The mean Dice of the ResTLU-Net was 95.81% (no need for denoise and recorrect), and the method required only approximately 30–60 s for one extraction task on an NVIDIA 1660S GPU. |
format | Online Article Text |
id | pubmed-8870262 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88702622022-02-25 A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure Wang, Qianshan Fei, Hong Abdu Nasher, Saddam Naji Xia, Xiaoluan Li, Haifang Brain Sci Article Accurately extracting brain tissue is a critical and primary step in brain neuroimaging research. Due to the differences in brain size and structure between humans and nonhuman primates, the performance of the existing tools for brain tissue extraction, working on macaque brain MRI, is constrained. A new transfer learning training strategy was utilized to address the limitations, such as insufficient training data and unsatisfactory model generalization ability, when deep neural networks processing the limited samples of macaque magnetic resonance imaging(MRI). First, the project combines two human brain MRI data modes to pre-train the neural network, in order to achieve faster training and more accurate brain extraction. Then, a residual network structure in the U-Net model was added, in order to propose a ResTLU-Net model that aims to improve the generalization ability of multiple research sites data. The results demonstrated that the ResTLU-Net, combined with the proposed transfer learning strategy, achieved comparable accuracy for the macaque brain MRI extraction tasks on different macaque brain MRI volumes that were produced by various medical centers. The mean Dice of the ResTLU-Net was 95.81% (no need for denoise and recorrect), and the method required only approximately 30–60 s for one extraction task on an NVIDIA 1660S GPU. MDPI 2022-02-12 /pmc/articles/PMC8870262/ /pubmed/35204023 http://dx.doi.org/10.3390/brainsci12020260 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Qianshan Fei, Hong Abdu Nasher, Saddam Naji Xia, Xiaoluan Li, Haifang A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure |
title | A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure |
title_full | A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure |
title_fullStr | A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure |
title_full_unstemmed | A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure |
title_short | A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure |
title_sort | macaque brain extraction model based on u-net combined with residual structure |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870262/ https://www.ncbi.nlm.nih.gov/pubmed/35204023 http://dx.doi.org/10.3390/brainsci12020260 |
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