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Journal of Imaging Informatics in Medicine

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16-06-2022 | Computed Tomography

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

Authors: Jinke Wang, Xiangyang Zhang, Peiqing Lv, Haiying Wang, Yuanzhi Cheng

Published in: Journal of Imaging Informatics in Medicine | Issue 6/2022

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.

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

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

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

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

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

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

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

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Title: Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT
Authors: Jinke Wang
Xiangyang Zhang
Peiqing Lv
Haiying Wang
Yuanzhi Cheng
Publication date: 16-06-2022
Publisher: Springer International Publishing
Keywords: Computed Tomography
Computed Tomography
Published in: Journal of Imaging Informatics in Medicine / Issue 6/2022
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-022-00668-x

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

Abstract

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