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On-line Access: 2021-06-11

Received: 2020-07-15

Revision Accepted: 2020-12-02

Crosschecked: 0000-00-00

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiaobing ZHANG

https://orcid.org/0000-0001-9344-2630

Shengdong NIE

https://orcid.org/0000-0001-7825-4455

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Journal of Zhejiang University SCIENCE B 2021 Vol.22 No.6 P.462-475

http://doi.org/10.1631/jzus.B2000381


3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks


Author(s):  Xiaobing ZHANG, Yin HU, Wen CHEN, Gang HUANG, Shengdong NIE

Affiliation(s):  School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; more

Corresponding email(s):   nsd4647@163.com

Key Words:  Glioma, Magnetic resonance imaging (MRI), Segmentation, Dense block, 2D convolutional neural networks (2D-CNNs)


Xiaobing ZHANG, Yin HU, Wen CHEN, Gang HUANG, Shengdong NIE. 3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks[J]. Journal of Zhejiang University Science B, 2021, 22(6): 462-475.

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%T 3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks
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%A Yin HU
%A Wen CHEN
%A Gang HUANG
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T1 - 3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks
A1 - Xiaobing ZHANG
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A1 - Wen CHEN
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A1 - Shengdong NIE
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DOI - 10.1631/jzus.B2000381


Abstract: 
To overcome the computational burden of processing three-dimensional (3D) medical scans and the lack of spatial information in two-dimensional (2D) medical scans, a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2D convolutional neural networks (2D-CNNs). In order to combine the low-level features and high-level features, we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process. Further, in order to resolve the problems of the blurred boundary of the glioma edema area, we superimposed and fused the T2-weighted fluid-attenuated inversion recovery (FLAIR) modal image and the T2-weighted (T2) modal image to enhance the edema section. For the loss function of network training, we improved the cross-entropy loss function to effectively avoid network over-fitting. On the Multimodal Brain Tumor Image segmentation Challenge (BraTS) datasets, our method achieves dice similarity coefficient values of 0.84, 0.82, and 0.83 on the BraTS2018 training; 0.82, 0.85, and 0.83 on the BraTS2018 validation; and 0.81, 0.78, and 0.83 on the BraTS2013 testing in terms of whole tumors, tumor cores, and enhancing cores, respectively. Experimental results showed that the proposed method achieved promising accuracy and fast processing, demonstrating good potential for clinical medicine.

集成多个密集连接二维卷积神经网络(2D-CNNs)分割模型的脑胶质瘤三维分割

概要:为了克服处理三维医学扫描的计算负担和二维医学扫描中空间信息的不足,本文提出了一种新的脑胶质瘤分割方法,该方法将三个密集连接的二维卷积神经网络(2D-CNN)分割模型的分割结果进行融合。为了将低级特征和高级特征组合在一起,本文在网络结构设计中添加了紧密连接的模块,这样在学习过程中随着网络层数的增加,低级特征将不会被遗漏。此外,为了解决神经胶质瘤水肿区域边界模糊的问题,我们叠加并融合了液体衰减反转恢复(FLAIR)模态图像和T2模态图像以增强水肿区域。对于网络训练的损失函数,我们改进了交叉熵损失函数,有效避免了网络过度拟合。本文在多模态脑肿瘤图像分割数据集(BraTS)上进行实验验证。其中,我们的方法在BraTS2018训练集上在整个肿瘤,肿瘤核心和增强肿瘤区域的Dice系数值分别达到了0.84、0.82和0.83;在BraTS2018验证集上达到0.82、0.85和0.83;在BraTS2013测试集上达到的0.81、0.78和0.83。实验结果表明,该方法具有良好的准确性和快速的处理能力,具有良好的临床应用前景。

关键词:脑胶质瘤;磁共振成像(MRI);分割;密集连接块;二维卷积神经网络(2D-CNNs)

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