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3D-MASNet: 3D Mixed-scale Asymmetric Convolutional Segmentation Network for 6-month-old Infant Brain MR Images

Zilong Zeng, Tengda Zhao, Lianglong Sun, Yihe Zhang, Mingrui Xia, Xuhong Liao, Jiaying Zhang, Dinggang Shen, Li Wang, Yong He
doi: https://doi.org/10.1101/2021.05.23.445294
Zilong Zeng
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
2Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
3IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
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Tengda Zhao
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
2Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
3IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
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  • For correspondence: yong.he@bnu.edu.cn tengdazhao@bnu.edu.cn
Lianglong Sun
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
2Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
3IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
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Yihe Zhang
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
2Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
3IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
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Mingrui Xia
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
2Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
3IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
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Xuhong Liao
4School of Systems Science, Beijing Normal University, Beijing 100875, China
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Jiaying Zhang
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
2Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
3IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
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Dinggang Shen
5School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
6Department of Research and Development, Shanghai Unitied Imaging Intelligence Co., Ltd., Shanghai 200030, China
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Li Wang
7Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
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Yong He
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
2Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
3IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
8Chinese Institute for Brain Research, Beijing 102206, China
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  • For correspondence: yong.he@bnu.edu.cn tengdazhao@bnu.edu.cn
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Abstract

Precise segmentation of infant brain 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • The introduction and discussion parts are updated. The method part is been reorganized. Figure 3 revised.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 24, 2022.
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3D-MASNet: 3D Mixed-scale Asymmetric Convolutional Segmentation Network for 6-month-old Infant Brain MR Images
Zilong Zeng, Tengda Zhao, Lianglong Sun, Yihe Zhang, Mingrui Xia, Xuhong Liao, Jiaying Zhang, Dinggang Shen, Li Wang, Yong He
bioRxiv 2021.05.23.445294; doi: https://doi.org/10.1101/2021.05.23.445294
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3D-MASNet: 3D Mixed-scale Asymmetric Convolutional Segmentation Network for 6-month-old Infant Brain MR Images
Zilong Zeng, Tengda Zhao, Lianglong Sun, Yihe Zhang, Mingrui Xia, Xuhong Liao, Jiaying Zhang, Dinggang Shen, Li Wang, Yong He
bioRxiv 2021.05.23.445294; doi: https://doi.org/10.1101/2021.05.23.445294

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