Computed-Tomography Image Segmentation of Cerebral Hemorrhage Based on Improved U-shaped Neural Network

HU Min; ZHOU Xiudong; HUANG Hongcheng; ZHANG Guanghua; TAO Yang

doi:10.11999/JEIT200996

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 127-137

HU Min, ZHOU Xiudong, HUANG Hongcheng, ZHANG Guanghua, TAO Yang. Computed-Tomography Image Segmentation of Cerebral Hemorrhage Based on Improved U-shaped Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 127-137. doi: 10.11999/JEIT200996

Citation:

HU Min, ZHOU Xiudong, HUANG Hongcheng, ZHANG Guanghua, TAO Yang. Computed-Tomography Image Segmentation of Cerebral Hemorrhage Based on Improved U-shaped Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 127-137. doi: 10.11999/JEIT200996

Citation:

HU Min, ZHOU Xiudong, HUANG Hongcheng, ZHANG Guanghua, TAO Yang. Computed-Tomography Image Segmentation of Cerebral Hemorrhage Based on Improved U-shaped Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 127-137. doi: 10.11999/JEIT200996

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Computed-Tomography Image Segmentation of Cerebral Hemorrhage Based on Improved U-shaped Neural Network

doi: 10.11999/JEIT200996

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Engineering Research Center of Communication Software, Chongqing 400065, China
3.
Department of Computer Science and Engineering, Taiyuan University, Taiyuan 030000, China

Funds: The National Key Research and Development Program of China(2019YFB2102001), The Research Project of Shanxi Scholarship Council of China (2020-149)

Received Date: 2020-11-25
Rev Recd Date: 2021-05-27

Available Online: 2021-08-16

Publish Date: 2022-01-10

Abstract

Abstract

In view of the problem of low segmentation accuracy caused by the multi-scale of the lesion location in Computed-Tomography (CT) images of cerebral hemorrhage, an image segmentation model based on Attention improved U-shaped neural Network plus (AU-Net+) is proposed. Firstly, the model uses the encoder in U-Net to encode the features of the CT image of cerebral hemorrhage, and applies the proposed Residual Octave Convolution (ROC) block to the jump connection part of the U-shaped neural network to make the features of different levels more blend well. Secondly, for the merged features, a hybrid attention mechanism is introduced to improve the feature extraction ability of the target area. Finally, the Dice loss function is improved to enhance further the feature learning of the model for small and medium-sized target regions in CT images of cerebral hemorrhage. To verify the performance of the model, the mIoU index is improved by 20.9%, 3.6%, 7.0%, 3.1% compared with U-Net, Attention U-Net, UNet++ and CE-Net respectively, which indicates that AU-Net+ model has better segmentation effect.

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References(19)

References

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