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Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT

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Abstract

This paper proposes a new network framework, which leverages EfficientNetB4, attention gate, and residual learning techniques to achieve automatic and accurate liver segmentation. First, we use EfficientNetB4 as the encoder to extract more feature information during the encoding stage. Then, an attention gate is introduced in the skip connection to eliminate irrelevant regions and highlight features of a specific segmentation task. Finally, to alleviate the problem of gradient vanishment, we replace the traditional convolution of the decoder with a residual block to improve the segmentation accuracy. We verified the proposed method on the LiTS17 and SLiver07 datasets and compared it with classical networks such as FCN, U-Net, attention U-Net, and attention Res-U-Net. In the Sliver07 evaluation, the proposed method achieved the best segmentation performance on all five standard metrics. Meanwhile, in the LiTS17 assessment, the best performance is obtained except for a slight inferior on RVD. The proposed method’s qualitative and quantitative results demonstrated its applicability in liver segmentation and proved its good prospect in computer-assisted liver segmentation.

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Data Availability

Datasets are publicly available.

Code Availability

The code of the proposed EAR-U-Net is available at https://github.com/ZhangXY-123/Model/blob/master/EAR_Unet.py.

Notes

  1. The code is publicly available at https://github.com/ZhangXY-123/EAR-Unet

  2. The dataset is publicly available at https://competitions.codalab.org/competitions/17094#results

  3. The dataset is publicly available at https://sliver07.grand-challenge.org/

  4. The code is available at https://github.com/shelhamer/fcn.berkeleyvision.org

  5. The code is available at https://github.com/JavisPeng/u_net_liver/blob/master/unet.py

  6. The code is available at https://github.com/Andy-zhujunwen/UNET-ZOO/blob/master/attention_unet.py

  7. The code is available at https://github.com/ZhangXY-123/Model/blob/master/Res_Att_Unet.py

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Funding

This work is supported by the National Nature Science Foundation (no. 61741106).

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Authors and Affiliations

  1. Department of Software Engineering, Harbin University of Science and Technology, No. 2006, Xueyuan Road, Shandong Province, Rongcheng City, 264300, China

    Jinke Wang

  2. School of Automation, Harbin University of Science and Technology, Harbin, 150080, China

    Jinke Wang, Xiangyang Zhang, Peiqing Lv & Haiying Wang

  3. School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China

    Yuanzhi Cheng

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  1. Jinke Wang
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  2. Xiangyang Zhang
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Correspondence to Jinke Wang.

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Wang, J., Zhang, X., Lv, P. et al. Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT. J Digit Imaging 35, 1479–1493 (2022). https://doi.org/10.1007/s10278-022-00668-x

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  • DOI: https://doi.org/10.1007/s10278-022-00668-x

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