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Open Access

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

Yizhan Fan1, Zhenchao Tao1, Jun Lin2,3,4, Huanhuan 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
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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 cancerimage segmentationcomputed tomography (CT) scan imageencoder-decoder network

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International Journal of Crowd Science
Volume 6 Issue 3,
September 2022
Pages 111-116
DOI: 10.26599/IJCS.2022.9100014
Cite this article:
Fan Y, Tao Z, Lin J, et al. An Encoder-Decoder Network for Automatic Clinical Target Volume Target Segmentation of Cervical Cancer in CT Images. International Journal of Crowd Science, 2022, 6(3): 111-116. https://doi.org/10.26599/IJCS.2022.9100014

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Received: 09 February 2022
Revised: 24 March 2022
Accepted: 06 April 2022
Published: 09 August 2022
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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