Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI

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成果类型：

期刊论文

作者：

Wong, Kelvin K. L.;Xu, Wanni;Ayoub, Muhammad;Fu, You-Lei;Xu, Huasen;...

通讯作者：

Wong, KKL;Xu, WN

作者机构：

[Chen, Weimin; Wong, Kelvin K. L.] Hunan City Univ, Sch Informat & Elect, Yiyang 413000, Peoples R China.

[Fu, You-Lei; Xu, Wanni] Natl Taiwan Normal Univ, Coll Technol & Engn, Taipei 106, Taiwan.

[Fu, You-Lei; Xu, Wanni] Nanchang Inst Technol, Dept Comp Informat Engn, Nanchang 330044, Peoples R China.

[Xu, Huasen] Shanghai Normal Univ, Dept Civil Engn, Shanghai 201418, Peoples R China.

[Ayoub, Muhammad] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Hunan, Peoples R China.

通讯机构：

[Wong, KKL ] H

[Xu, WN ] N

Hunan City Univ, Sch Informat & Elect, Yiyang 413000, Peoples R China.

Natl Taiwan Normal Univ, Coll Technol & Engn, Taipei 106, Taiwan.

语种：

英文

关键词：

Brain;Computed tomography;Magnetic resonance imaging;Medical image segmentation;Bi-Directional Convolutional LSTM;Multiple cerebral infarctions

期刊：

Computer Methods and Programs in Biomedicine

ISSN：

0169-2607

年：

2023

卷：

238

页码：

107602

DOI：

10.1016/j.cmpb.2023.107602

机构署名：

本校为第一且通讯机构

摘要：

Background and objective: Traditional disease diagnosis is usually performed by experienced physicians, but misdiagnosis or missed diagnosis still exists. Exploring the relationship between changes in the cor-pus callosum and multiple brain infarcts requires extracting corpus callosum features from brain image data, which requires addressing three key issues. (1) automation, (2) completeness, and (3) accuracy. Residual learning can facilitate network training, Bi-Directional Convolutional L STM (BDC-L STM) can ex-ploit interlayer spatial dependencies, and HDC can expand the receptive domain wi...

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Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI

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