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Open AccessArticle
Dual Attention-Based 3D U-Net Liver Segmentation Algorithm on CT Images
by
Benyue Zhang
Benyue Zhang 1,2,
Shi Qiu
Shi Qiu 1,* and
Ting Liang
Ting Liang 3,*
1
Key Laboratory of Spectral Imaging Technology CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
2
School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100408, China
3
Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710119, China
*
Authors to whom correspondence should be addressed.
Bioengineering 2024, 11(7), 737; https://doi.org/10.3390/bioengineering11070737 (registering DOI)
Submission received: 11 June 2024
/
Revised: 11 July 2024
/
Accepted: 17 July 2024
/
Published: 20 July 2024
Abstract
The liver is a vital organ in the human body, and CT images can intuitively display its morphology. Physicians rely on liver CT images to observe its anatomical structure and areas of pathology, providing evidence for clinical diagnosis and treatment planning. To assist physicians in making accurate judgments, artificial intelligence techniques are adopted. Addressing the limitations of existing methods in liver CT image segmentation, such as weak contextual analysis and semantic information loss, we propose a novel Dual Attention-Based 3D U-Net liver segmentation algorithm on CT images. The innovations of our approach are summarized as follows: (1) We improve the 3D U-Net network by introducing residual connections to better capture multi-scale information and alleviate semantic information loss. (2) We propose the DA-Block encoder structure to enhance feature extraction capability. (3) We introduce the CBAM module into skip connections to optimize feature transmission in the encoder, reducing semantic gaps and achieving accurate liver segmentation. To validate the effectiveness of the algorithm, experiments were conducted on the LiTS dataset. The results showed that the Dice coefficient and HD95 index for liver images were 92.56% and 28.09 mm, respectively, representing an improvement of 0.84% and a reduction of 2.45 mm compared to 3D Res-UNet.
Share and Cite
MDPI and ACS Style
Zhang, B.; Qiu, S.; Liang, T.
Dual Attention-Based 3D U-Net Liver Segmentation Algorithm on CT Images. Bioengineering 2024, 11, 737.
https://doi.org/10.3390/bioengineering11070737
AMA Style
Zhang B, Qiu S, Liang T.
Dual Attention-Based 3D U-Net Liver Segmentation Algorithm on CT Images. Bioengineering. 2024; 11(7):737.
https://doi.org/10.3390/bioengineering11070737
Chicago/Turabian Style
Zhang, Benyue, Shi Qiu, and Ting Liang.
2024. "Dual Attention-Based 3D U-Net Liver Segmentation Algorithm on CT Images" Bioengineering 11, no. 7: 737.
https://doi.org/10.3390/bioengineering11070737
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