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Cervical cancer is a common gynecological cancer, and its common treatment method radiotherapy depends on target area delineation. The manual delineation work takes a long time and has low accuracy, so automating such delineation is important. At present, some traditional image segmentation algorithms for target area delineation have low accuracy rates. Deep learning algorithms also face some difficulties, such as insufficient data and long training time. As the popular network used in medical image segmentation, U-net still has several disadvantages when handling small targets with unclear boundaries. According to the characteristics of the clinical target volume target segmentation task of cervical cancer, this study modified the U-net structure and optimized the training loss to improve the accuracy of small target detection. The modified structure could handle target boundaries well with operations such as bilinear upsampling. Finally, the proposed algorithm was evaluated on the dataset and compared with several deep learning-based algorithms. Results indicate that the proposed approach has certain superiority.


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An Encoder-Decoder Network for Automatic Clinical Target Volume Target Segmentation of Cervical Cancer in CT Images

Show Author's information Yizhan Fan1Zhenchao Tao1Jun Lin2,3,4Huanhuan Chen1( )
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
Alibaba-NTU Singapore Joint Research Institute, Nanyang Technological University, Singapore 639798, Singapore
Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Jinan 250101, China
China-Singapore International Joint Research Institute, Guangzhou 510555, China

Abstract

Cervical cancer is a common gynecological cancer, and its common treatment method radiotherapy depends on target area delineation. The manual delineation work takes a long time and has low accuracy, so automating such delineation is important. At present, some traditional image segmentation algorithms for target area delineation have low accuracy rates. Deep learning algorithms also face some difficulties, such as insufficient data and long training time. As the popular network used in medical image segmentation, U-net still has several disadvantages when handling small targets with unclear boundaries. According to the characteristics of the clinical target volume target segmentation task of cervical cancer, this study modified the U-net structure and optimized the training loss to improve the accuracy of small target detection. The modified structure could handle target boundaries well with operations such as bilinear upsampling. Finally, the proposed algorithm was evaluated on the dataset and compared with several deep learning-based algorithms. Results indicate that the proposed approach has certain superiority.

Keywords:

cervical cancer, image segmentation, computed tomography (CT) scan image, encoder-decoder network
Received: 09 February 2022 Revised: 24 March 2022 Accepted: 06 April 2022 Published: 09 August 2022 Issue date: September 2022
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Publication history

Received: 09 February 2022
Revised: 24 March 2022
Accepted: 06 April 2022
Published: 09 August 2022
Issue date: September 2022

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© The author(s) 2022

Acknowledgements

Acknowledgment

This work was supported by the National Natural Science Foundation of China (Nos. 62176245 and 62137002), Key Research and Development Program of Anhui Province (No. 202104a05020011), Key Science and Technology Special Project of Anhui Province (No. 202103a07020002), and the Fundamental Research Funds for the Central Universities.

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