Cargando…

3D‐MASNet: 3D mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain MR images

Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6‐month‐old infants, the extremely low‐intensity contrast caused b...

Descripción completa

Detalles Bibliográficos
Autores principales: Zeng, Zilong, Zhao, Tengda, Sun, Lianglong, Zhang, Yihe, Xia, Mingrui, Liao, Xuhong, Zhang, Jiaying, Shen, Dinggang, Wang, Li, He, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921327/
https://www.ncbi.nlm.nih.gov/pubmed/36515219
http://dx.doi.org/10.1002/hbm.26174
_version_ 1784887285346467840
author Zeng, Zilong
Zhao, Tengda
Sun, Lianglong
Zhang, Yihe
Xia, Mingrui
Liao, Xuhong
Zhang, Jiaying
Shen, Dinggang
Wang, Li
He, Yong
author_facet Zeng, Zilong
Zhao, Tengda
Sun, Lianglong
Zhang, Yihe
Xia, Mingrui
Liao, Xuhong
Zhang, Jiaying
Shen, Dinggang
Wang, Li
He, Yong
author_sort Zeng, Zilong
collection PubMed
description Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6‐month‐old infants, the extremely low‐intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single‐scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed‐scale asymmetric convolutional segmentation network (3D‐MASNet) framework for brain MR images of 6‐month‐old infants. We replaced the traditional convolutional layer of an existing to‐be‐trained network with a 3D mixed‐scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1‐ and T2‐weighted images of 23 6‐month‐old infants from iSeg‐2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC‐3D‐FCN) and the highest performance in the Dense U‐Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg‐2019 Grand Challenge. Thus, the proposed 3D‐MASNet can improve the accuracy of existing CNNs‐based segmentation models as a plug‐and‐play solution that offers a promising technique for future infant brain MRI studies.
format Online
Article
Text
id pubmed-9921327
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher John Wiley & Sons, Inc.
record_format MEDLINE/PubMed
spelling pubmed-99213272023-02-13 3D‐MASNet: 3D mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain MR images Zeng, Zilong Zhao, Tengda Sun, Lianglong Zhang, Yihe Xia, Mingrui Liao, Xuhong Zhang, Jiaying Shen, Dinggang Wang, Li He, Yong Hum Brain Mapp Research Articles Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6‐month‐old infants, the extremely low‐intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single‐scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed‐scale asymmetric convolutional segmentation network (3D‐MASNet) framework for brain MR images of 6‐month‐old infants. We replaced the traditional convolutional layer of an existing to‐be‐trained network with a 3D mixed‐scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1‐ and T2‐weighted images of 23 6‐month‐old infants from iSeg‐2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC‐3D‐FCN) and the highest performance in the Dense U‐Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg‐2019 Grand Challenge. Thus, the proposed 3D‐MASNet can improve the accuracy of existing CNNs‐based segmentation models as a plug‐and‐play solution that offers a promising technique for future infant brain MRI studies. John Wiley & Sons, Inc. 2022-12-14 /pmc/articles/PMC9921327/ /pubmed/36515219 http://dx.doi.org/10.1002/hbm.26174 Text en © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research Articles
Zeng, Zilong
Zhao, Tengda
Sun, Lianglong
Zhang, Yihe
Xia, Mingrui
Liao, Xuhong
Zhang, Jiaying
Shen, Dinggang
Wang, Li
He, Yong
3D‐MASNet: 3D mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain MR images
title 3D‐MASNet: 3D mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain MR images
title_full 3D‐MASNet: 3D mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain MR images
title_fullStr 3D‐MASNet: 3D mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain MR images
title_full_unstemmed 3D‐MASNet: 3D mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain MR images
title_short 3D‐MASNet: 3D mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain MR images
title_sort 3d‐masnet: 3d mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain mr images
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921327/
https://www.ncbi.nlm.nih.gov/pubmed/36515219
http://dx.doi.org/10.1002/hbm.26174
work_keys_str_mv AT zengzilong 3dmasnet3dmixedscaleasymmetricconvolutionalsegmentationnetworkfor6montholdinfantbrainmrimages
AT zhaotengda 3dmasnet3dmixedscaleasymmetricconvolutionalsegmentationnetworkfor6montholdinfantbrainmrimages
AT sunlianglong 3dmasnet3dmixedscaleasymmetricconvolutionalsegmentationnetworkfor6montholdinfantbrainmrimages
AT zhangyihe 3dmasnet3dmixedscaleasymmetricconvolutionalsegmentationnetworkfor6montholdinfantbrainmrimages
AT xiamingrui 3dmasnet3dmixedscaleasymmetricconvolutionalsegmentationnetworkfor6montholdinfantbrainmrimages
AT liaoxuhong 3dmasnet3dmixedscaleasymmetricconvolutionalsegmentationnetworkfor6montholdinfantbrainmrimages
AT zhangjiaying 3dmasnet3dmixedscaleasymmetricconvolutionalsegmentationnetworkfor6montholdinfantbrainmrimages
AT shendinggang 3dmasnet3dmixedscaleasymmetricconvolutionalsegmentationnetworkfor6montholdinfantbrainmrimages
AT wangli 3dmasnet3dmixedscaleasymmetricconvolutionalsegmentationnetworkfor6montholdinfantbrainmrimages
AT heyong 3dmasnet3dmixedscaleasymmetricconvolutionalsegmentationnetworkfor6montholdinfantbrainmrimages