Cargando…
HC-Net: A hybrid convolutional network for non-human primate brain extraction
Brain extraction (skull stripping) is an essential step in the magnetic resonance imaging (MRI) analysis of brain sciences. However, most of the current brain extraction methods that achieve satisfactory results for human brains are often challenged by non-human primate brains. Due to the small samp...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947775/ https://www.ncbi.nlm.nih.gov/pubmed/36846727 http://dx.doi.org/10.3389/fncom.2023.1113381 |
_version_ | 1784892634994573312 |
---|---|
author | Fei, Hong Wang, Qianshan Shang, Fangxin Xu, Wenyi Chen, Xiaofeng Chen, Yifei Li, Haifang |
author_facet | Fei, Hong Wang, Qianshan Shang, Fangxin Xu, Wenyi Chen, Xiaofeng Chen, Yifei Li, Haifang |
author_sort | Fei, Hong |
collection | PubMed |
description | Brain extraction (skull stripping) is an essential step in the magnetic resonance imaging (MRI) analysis of brain sciences. However, most of the current brain extraction methods that achieve satisfactory results for human brains are often challenged by non-human primate brains. Due to the small sample characteristics and the nature of thick-slice scanning of macaque MRI data, traditional deep convolutional neural networks (DCNNs) are unable to obtain excellent results. To overcome this challenge, this study proposed a symmetrical end-to-end trainable hybrid convolutional neural network (HC-Net). It makes full use of the spatial information between adjacent slices of the MRI image sequence and combines three consecutive slices from three axes for 3D convolutions, which reduces the calculation consumption and promotes accuracy. The HC-Net consists of encoding and decoding structures of 3D convolutions and 2D convolutions in series. The effective use of 2D convolutions and 3D convolutions relieves the underfitting of 2D convolutions to spatial features and the overfitting of 3D convolutions to small samples. After evaluating macaque brain data from different sites, the results showed that HC-Net performed better in inference time (approximately 13 s per volume) and accuracy (mean Dice coefficient reached 95.46%). The HC-Net model also had good generalization ability and stability in different modes of brain extraction tasks. |
format | Online Article Text |
id | pubmed-9947775 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99477752023-02-24 HC-Net: A hybrid convolutional network for non-human primate brain extraction Fei, Hong Wang, Qianshan Shang, Fangxin Xu, Wenyi Chen, Xiaofeng Chen, Yifei Li, Haifang Front Comput Neurosci Neuroscience Brain extraction (skull stripping) is an essential step in the magnetic resonance imaging (MRI) analysis of brain sciences. However, most of the current brain extraction methods that achieve satisfactory results for human brains are often challenged by non-human primate brains. Due to the small sample characteristics and the nature of thick-slice scanning of macaque MRI data, traditional deep convolutional neural networks (DCNNs) are unable to obtain excellent results. To overcome this challenge, this study proposed a symmetrical end-to-end trainable hybrid convolutional neural network (HC-Net). It makes full use of the spatial information between adjacent slices of the MRI image sequence and combines three consecutive slices from three axes for 3D convolutions, which reduces the calculation consumption and promotes accuracy. The HC-Net consists of encoding and decoding structures of 3D convolutions and 2D convolutions in series. The effective use of 2D convolutions and 3D convolutions relieves the underfitting of 2D convolutions to spatial features and the overfitting of 3D convolutions to small samples. After evaluating macaque brain data from different sites, the results showed that HC-Net performed better in inference time (approximately 13 s per volume) and accuracy (mean Dice coefficient reached 95.46%). The HC-Net model also had good generalization ability and stability in different modes of brain extraction tasks. Frontiers Media S.A. 2023-02-09 /pmc/articles/PMC9947775/ /pubmed/36846727 http://dx.doi.org/10.3389/fncom.2023.1113381 Text en Copyright © 2023 Fei, Wang, Shang, Xu, Chen, Chen and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Fei, Hong Wang, Qianshan Shang, Fangxin Xu, Wenyi Chen, Xiaofeng Chen, Yifei Li, Haifang HC-Net: A hybrid convolutional network for non-human primate brain extraction |
title | HC-Net: A hybrid convolutional network for non-human primate brain extraction |
title_full | HC-Net: A hybrid convolutional network for non-human primate brain extraction |
title_fullStr | HC-Net: A hybrid convolutional network for non-human primate brain extraction |
title_full_unstemmed | HC-Net: A hybrid convolutional network for non-human primate brain extraction |
title_short | HC-Net: A hybrid convolutional network for non-human primate brain extraction |
title_sort | hc-net: a hybrid convolutional network for non-human primate brain extraction |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947775/ https://www.ncbi.nlm.nih.gov/pubmed/36846727 http://dx.doi.org/10.3389/fncom.2023.1113381 |
work_keys_str_mv | AT feihong hcnetahybridconvolutionalnetworkfornonhumanprimatebrainextraction AT wangqianshan hcnetahybridconvolutionalnetworkfornonhumanprimatebrainextraction AT shangfangxin hcnetahybridconvolutionalnetworkfornonhumanprimatebrainextraction AT xuwenyi hcnetahybridconvolutionalnetworkfornonhumanprimatebrainextraction AT chenxiaofeng hcnetahybridconvolutionalnetworkfornonhumanprimatebrainextraction AT chenyifei hcnetahybridconvolutionalnetworkfornonhumanprimatebrainextraction AT lihaifang hcnetahybridconvolutionalnetworkfornonhumanprimatebrainextraction |